Month: August 2023

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LATEST Report of Cloud-based Database Market 2023-2030 | Segmentation by: Types, Applications, Players, Regions

Cloud-based Database Market Insights 2023:

TOP Dynamic players in the Cloud-based Database Market (Couchbase, Cassandra, Oracle, Amazon Web Services, Google, Teradata, Alibaba, Rackspace Hosting, Salesforce, MongoDB, Microsoft, IBM, SAP, Tencent) are driving the market’s growth in the ICT sector during the forecast period of 2023-2030. The Market Development Report has segmented the global Cloud-based Database market report based on Type (NoSQL Database, SQL Database) and Application (Large Enterprises, Small and Medium Business).

Updated Report of {120Pages}

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Customer Attention:

1. How will you analyse the competition analysis between top key players included in the report?

With the aim of clearly revealing the competitive state of the industry, we concretely analyse not only the leading plyers that have a voice on a global scale, but also the regional small and medium-sized players that play key roles and have plenty of potential growth.Key players in the global Cloud-based Database market are covered in Chapter 3 and 8:

• Couchbase
• Cassandra
• Oracle
• Amazon Web Services
• Google
• Teradata
• Alibaba
• Rackspace Hosting
• Salesforce
• MongoDB
• Microsoft
• IBM
• SAP
• Tencent

Brief Picture About Cloud-based Database Market:

Market Overview of Global Cloud-based Database market:
According to our latest research, the global Cloud-based Database market looks promising in the next 5 years. As of 2022, the global Cloud-based Database market was estimated at USD 7614.33 million, and it’s anticipated to reach USD 27771.49 million in 2028, with a CAGR of 24.07% during the forecast years.

A cloud database is a type of database service that is built, deployed and delivered through a cloud platform. It is primarily a cloud Platform as a Service (PaaS) delivery model that allows organizations, end users and their applications to store, manage and retrieve data from the cloud.

This report covers a research time span from 2018 to 2028, and presents a deep and comprehensive analysis of the global Cloud-based Database market, with a systematical description of the status quo and trends of the whole market, a close look into the competitive landscape of the major players, and a detailed elaboration on segment markets by type, by application and by region. (Ask for Sample of Report)

2. Does this report consider the impact of COVID-19 and the Russia-Ukraine war on the Cloud-based Database market?As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and raw material price system, we have definitely taken them into consideration throughout the research, and in Chapters 1.7, 2.7, 4.1, 7.5, 8.7, we elaborate at full length on the impact of the pandemic and the war on the Cloud-based Database Industry.

To Know How Covid-19 Pandemic and Russia Ukraine War Will Impact This Market- REQUEST SAMPLE

3. What are the major applications and type of Cloud-based Database Market?

On the basis of product typethis report displays the production, revenue, price, market share and growth rate of each type, primarily split into:

• NoSQL Database
• SQL Database

On the basis of the end users/applicationsthis report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:

• Large Enterprises
• Small and Medium Business

You will get detailed information regarding types and applications in Chapter 4,5 and 6

4. What is a major information sources?Both Primary and Secondary data sources are being used while compiling the report.
   Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users. Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.

   Please find a more complete list of data sources in Chapters 11

Inquire more and share questions if any before the purchase on this report at-https://www.marketgrowthreports.com/enquiry/pre-order-enquiry/23067367

This Cloud-based Database Market Research/Analysis Report give Answers to following Questions:

• How Porter’s Five Forces model helps you to study Cloud-based Database Market?
• What Was Global Market Status of Cloud-based Database Market? What Was Capacity, Production Value, Cost and PROFIT of Cloud-based Database Market?
• What is the major industry objective of the report? What are the critical discoveries of the report?
• What are the TOP 10 KEY PLAYERS of Cloud-based Database Market?

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Detailed Table Of Content of Global Cloud-based Database Market Insights and Forecast to 2030

1 Market Overview

1.1 Product Overview and Scope of Cloud-based Database

1.2 Classification of Cloud-based Database by Type

1.2.1 Overview: Global Cloud-based Database Market Size by Type: 2017 Versus 2022 Versus 2030

1.2.2 Global Cloud-based Database Revenue Market Share by Type in 2022

1.3 Global Cloud-based Database Market by Application

1.4 Global Cloud-based Database Market Size and Forecast

1.5 Global Cloud-based Database Market Size and Forecast by Region

1.6 Market Drivers, Restraints and Trends

1.6.1 Cloud-based Database Market Drivers

1.6.2 Cloud-based Database Market Restraints

1.6.3 Cloud-based Database Trends Analysis

2 Company Profiles

2.1 Company

2.1.1 Company Details

2.1.2 Company Major Business

2.1.3 Company Cloud-based Database Product and Solutions

2.1.4 Company Cloud-based Database Revenue, Gross Margin and Market Share (2019, 2020, 2022 and 2023)

2.1.5 Company Recent Developments and Future Plans

3 Market Competition, by Players

3.1 Global Cloud-based Database Revenue and Share by Players (2019,2020,2022, and 2023)

3.2 Market Concentration Rate

3.2.1 Top3 Cloud-based Database Players Market Share in 2022

3.2.2 Top 10 Cloud-based Database Players Market Share in 2022

3.2.3 Market Competition Trend

3.3 Cloud-based Database Players Head Office, Products and Services Provided

3.4 Cloud-based Database Mergers and Acquisitions

3.5 Cloud-based Database New Entrants and Expansion Plans

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4 Market Size Segment by Type

4.1 Global Cloud-based Database Revenue and Market Share by Type (2017-2023)

4.2 Global Cloud-based Database Market Forecast by Type (2023-2030)

5 Market Size Segment by Application

5.1 Global Cloud-based Database Revenue Market Share by Application (2017-2023)

5.2 Global Cloud-based Database Market Forecast by Application (2023-2030)

6 Regions by Country, by Type, and by Application

6.1 Cloud-based Database Revenue by Type (2017-2030)

6.2 Cloud-based Database Revenue by Application (2017-2030)

6.3 Cloud-based Database Market Size by Country

6.3.1 Cloud-based Database Revenue by Country (2017-2030)

6.3.2 United States Cloud-based Database Market Size and Forecast (2017-2030)

6.3.3 Canada Cloud-based Database Market Size and Forecast (2017-2030)

6.3.4 Mexico Cloud-based Database Market Size and Forecast (2017-2030)

7 Research Findings and Conclusion

8 Appendix

8.1 Methodology

8.2 Research Process and Data Source

8.3 Disclaimer

9 Research Methodology

10 Conclusion

Continued…

Reasons to buy this report:

• To get a comprehensive overview of the Polling Software Market
• To gain wide ranging information about the top players in this industry, their product portfolios, and key strategies adopted by the players.
• To gain insights of the countries/regions in the Polling Software Market.

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Press Release Distributed by The Express Wire

To view the original version on The Express Wire visit Cloud-based Database Market Players to drive Merges and Acquisition Strategies to Expand Market Share, 2023-2030

Article originally posted on mongodb google news. Visit mongodb google news

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When deciding whether to buy, sell, or hold a stock, investors often rely on analyst recommendations. Media reports about rating changes by these brokerage-firm-employed (or sell-side) analysts often influence a stock’s price, but are they really important?

Let’s take a look at what these Wall Street heavyweights have to say about MongoDB (MDB) before we discuss the reliability of brokerage recommendations and how to use them to your advantage.

MongoDB currently has an average brokerage recommendation (ABR) of 1.50, on a scale of 1 to 5 (Strong Buy to Strong Sell), calculated based on the actual recommendations (Buy, Hold, Sell, etc.) made by 22 brokerage firms. An ABR of 1.50 approximates between Strong Buy and Buy.

Of the 22 recommendations that derive the current ABR, 16 are Strong Buy and three are Buy. Strong Buy and Buy respectively account for 72.7% and 13.6% of all recommendations.

Check price target & stock forecast for MongoDB here>>>

While the ABR calls for buying MongoDB, it may not be wise to make an investment decision solely based on this information. Several studies have shown limited to no success of brokerage recommendations in guiding investors to pick stocks with the best price increase potential.

Are you wondering why? The vested interest of brokerage firms in a stock they cover often results in a strong positive bias of their analysts in rating it. Our research shows that for every “Strong Sell” recommendation, brokerage firms assign five “Strong Buy” recommendations.

This means that the interests of these institutions are not always aligned with those of retail investors, giving little insight into the direction of a stock’s future price movement. It would therefore be best to use this information to validate your own analysis or a tool that has proven to be highly effective at predicting stock price movements.

With an impressive externally audited track record, our proprietary stock rating tool, the Zacks Rank, which classifies stocks into five groups, ranging from Zacks Rank #1 (Strong Buy) to Zacks Rank #5 (Strong Sell), is a reliable indicator of a stock’s near -term price performance. So, validating the Zacks Rank with ABR could go a long way in making a profitable investment decision.

Zacks Rank Should Not Be Confused With ABR

In spite of the fact that Zacks Rank and ABR both appear on a scale from 1 to 5, they are two completely different measures.

The ABR is calculated solely based on brokerage recommendations and is typically displayed with decimals (example: 1.28). In contrast, the Zacks Rank is a quantitative model allowing investors to harness the power of earnings estimate revisions. It is displayed in whole numbers — 1 to 5.

It has been and continues to be the case that analysts employed by brokerage firms are overly optimistic with their recommendations. Because of their employers’ vested interests, these analysts issue more favorable ratings than their research would support, misguiding investors far more often than helping them.

In contrast, the Zacks Rank is driven by earnings estimate revisions. And near-term stock price movements are strongly correlated with trends in earnings estimate revisions, according to empirical research.

Furthermore, the different grades of the Zacks Rank are applied proportionately across all stocks for which brokerage analysts provide earnings estimates for the current year. In other words, at all times, this tool maintains a balance among the five ranks it assigns.

Another key difference between the ABR and Zacks Rank is freshness. The ABR is not necessarily up-to-date when you look at it. But, since brokerage analysts keep revising their earnings estimates to account for a company’s changing business trends, and their actions get reflected in the Zacks Rank quickly enough, it is always timely in indicating future price movements.

Is MDB a Good Investment?

Looking at the earnings estimate revisions for MongoDB, the Zacks Consensus Estimate for the current year has remained unchanged over the past month at $1.51.

Analysts’ steady views regarding the company’s earnings prospects, as indicated by an unchanged consensus estimate, could be a legitimate reason for the stock to perform in line with the broader market in the near term.

The size of the recent change in the consensus estimate, along with three other factors related to earnings estimates, has resulted in a Zacks Rank #3 (Hold) for MongoDB. You can see the complete list of today’s Zacks Rank #1 (Strong Buy) stocks here >>>>

It may therefore be prudent to be a little cautious with the Buy-equivalent ABR for MongoDB.

Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report

MongoDB, Inc. (MDB) : Free Stock Analysis Report

To read this article on Zacks.com click here.

Zacks Investment Research

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Article originally posted on InfoQ. Visit InfoQ

Transcript

McGinnis: This talk really is about high-trust teams in a remote world. My entire experience, to have capable humans, the right focus is helping them have high trust, and everything else will come naturally. I want to start with a story. You’ve been at home for three weeks after the offices have temporarily closed in April 2020, making yourself a coffee. When you notice an email on your phone, an email from the CEO, an email from Tobi, “We’re going digital by default, effective immediately, forever.” How quite heavy of emotions. Disbelief, “We can’t be going remote, COVID is supposed to be done in a couple of weeks, or maybe a couple of months.” Surprise, “We can’t be making this decision, this early.” Uncertainty, “I have explicitly avoided working for a remote company my entire career. What does this mean for me?” Despite all of that, a bit of relief too, I at least know my future now. I can start figuring stuff out. I can get my office set up. I can figure out how to be successful, how to thrive in a remote environment, if that’s what I’m going to do. There were so many other emotions. Do you remember how you felt when this happened to you? Did it happen to you? Are you still figuring out if it should happen to you? Despite a lot of uncertainty, I’m really glad that we made the really hard decision to commit to going fully remote, even if it meant basically everything had to change.

Background & Outline

That change is what I’m here to talk with you. The lessons and reflections learned and felt over the past two and a half years. It all starts with one core idea, intentionality. To make that a little bit more concrete, there are three areas of focus that I think are worth our time together. First, foundational trust, second, first impressions, and third, good digital conversations. I’m Jesse. I’m a Senior Dev Lead at Shopify, which means I lead other leads who also sometimes lead leads, which means I get to think about stuff like this and act on stuff like this all of the time. I’ve been at Shopify for over six years now. Every year, there’s an insane amount of change in this rocket ship of a company. The last two years have been a profound change, something more dramatic than I have felt in my entire tenure at this place.

Intentionality

It’s something I want to share lessons from, which is really all centered on being intentional. You’re going to hear me say that a lot, because if you take nothing else from this, intentionality, being intentional with what you do and how you do it, and who you do it with, is what makes digital something good, something you can thrive in. Intentionality has always mattered, but now it’s necessary. Again, why am I focusing so much, I’ve already said it a dozen times. It’s because a lot of good things that we get when we are in the same physical space, colocated for free, don’t translate automatically to digital. Casual interactions that endear trust and humanize each other. Hallway conversations let us break out of the bubble, find out that this project by this other team is actually about to do something that impedes or builds off of what we’re doing that we didn’t even know existed. The physical vibes of when you walk into a room, and you get a sense of like, everyone is feeling on edge, or, everyone’s super giddy and excited, that you can adjust to, that you can learn from. All of those based off of the social physical cues that we as humans, over a millennia of evolution have been tuned to pick up on, that just, unfortunately, do not translate over this digital environment. All of this and a lot more is stuff that you now have to create intentional spaces for. Things for that watercooler conversation. Things that endear and build trust in your teams. Opportunities for cross-team, cross-org, cross-discipline connection, and spaces to just be social, to be human together.

Let’s round this all in an example that we can visit again, of what it was like in office, and how we can maybe recreate that in person with everything that we’re talking about. You walk into the office patio, a favorite place to start the day. You see some of your team together at a table. You pull up a seat, you feel excitement. Again, you start the day with some folks that you call friends. Emily, in the middle there is already deep into a story about her weekend scuba diving trip up north. First, you didn’t even know that she was a scuba diver. Second, this is something you’ve always wanted to explore doing, so you take a mental note. This is something you can pick up in your one-on-one tomorrow to get closer together through shared interests and curiosity. The conversation continues and eventually makes its way to Adrian, who mentions, it’s been a tiring weekend. He mentions it very quietly, and really forcefully redirects the conversation back to Emily. “You’ve been diving up north, isn’t that cold?” The conversation picks up and carries on. You take note for your one-on-one later today. You’ve noticed the last few weeks that Adrian has been distracted. It’s really out of character for him to so quickly brush away in a conversation like this. You want to dig into it, so you can be there to support him both as a colleague, but also as a friend. On and on the conversation goes, so many nuggets, so many opportunities for trust and relationship building. It’s all in this natural, authentic, ad hoc, unplanned conversation. This happens dozens of times every single day, when you’re together in-person. How do we recreate some of this goodness, when we’re all remote? Let’s find out together.

Focus On Trust

We’re going to start our journey with a focus on trust. Trust is the foundation of a high-performing team, whether you’re remote or in-person. It’s just more important when you’re remote. I find myself going back constantly to The Five Dysfunctions of a Team. I really genuinely believe that trust is the foundation of a high-performing team. It’s so worth huge investments into. Even before digital, I found much of what I’m about to share extremely important to how I set teams up for success. It’s just that trust as humans is foundational, before you can build trust as a team, before you can build up that ladder. What I mean is that you need to trust the person. You need to know who they are. You need to have a sense of what they bring to the table, why they’re here. That’s just hard to do in a remote environment. Why is this trust something that I have talked about so much, and I’m going to talk a lot more about? Why is there so much emphasis here? There’s a lot of reasons, but I want to focus on three.

First, it gives you shared ground, shared experiences for when things get shaky. It gives you a space for challenges and mistakes to become learning experiences. That trust allows for and enables and facilitates empathy. In remote, all of this together is just so critical. Our communications are just fundamentally more fragmented, and less nuanced, and mistakes are going to happen. This human trust is what allows us to recover and learn and evolve from that. Which brings me to my first major takeaway, trust as humans. Build that. This is foundational for high-performing teams. It’s foundational to create Slack in your leadership, in your organization, as mistakes are hit and learned and grown from. This trust underscores everything else that you’re going to try. How? We need intentional space for relationship building. I think there are three flavors of space that are really important and necessary to build. First, we need to dedicate time for socializing and fun. This is going to feel weird at first. You can’t book time to have fun, you have to in a remote world. A few little tactical pieces, just rotate the organizer. Make it optional, but put it in a time that’s not hard to attend. Think end of day Friday, or if you have a meeting, free day at the end of the day. Lean into the environment. Embrace the fact that this is a digital social event.

That gets into some really good examples of activities that I use and my teams use. First, for our regular recurring social time, there’s a giant roster of games you can play online that I’ve just taken a screenshot from our Slack for you to lean on. Things like Codenames, or GeoGuessr, are all digital. They have built great facilities around it. They’re the type of game that creates space for social interaction, while still having something to do. It’s really important in digital environments to have that when you have a large group. Lean into it. Find those low-barrier, low-entry, fun activities you can have on a recurring basis. Don’t stop there. Also, sometimes go big, just like you would when you’re in person. Have a remote cooking or drink class. Book a digital escape room. Book a visit from the zoo. You can have these wonderful Zoom calls where zoo caretakers bring exotic and interesting animals onto the call, introduce their name, tell their story. Everyone gets to ask questions about the animal. It’s a lot of fun, and it’s a really pleasant surprise. It helps keep this digital social interaction fresh, interesting, and engaging.

We can’t have only fun, it’s very surface. We need to sometimes get a bit deeper. We have that initial trust now, we can start layering in a little bit of depth. As you layer in depth, facilitation becomes more important. What these activities do, though, is they create room and space for us to get to know each other beyond small talk, to know who we are, to have some real conversations. The first one that I really love, because I think it’s just a beautifully curated list, is New York Times Questions That Lead to Love. It ranges from fairly surface level to very deep questions. You can do one or two a week. Go through them at random or in order. It creates an opportunity to share a little bit more about yourself, and how you think about the world, and get to know each other as humans. Just to showcase the range. We have things like, would you like to be famous? In what way? What’s great about those questions is it has that open-ended piece that invites a little bit behind the curtain, a little bit about who the human is. Then we also have things that can be heavier, like complete this sentence, “I wish I had someone with whom I could share.” It’s a deep question. You can answer at a very surface level, but you can also go really deep into things that you’re craving or missing in your life that don’t regularly get pulled up in our conversations. That question can create beautiful connection amongst the team, amongst the people, and really showcase who they are. There’s another prompt that I love called Ungame. It’s not as curated as The New York Times questions, but it gives you hundreds to thousands of questions of this nature. It’s a really great deck to just buy a version of, have handy for these real talk conversations, to give those prompts that enable us to go a little bit deeper.

I want to go even deeper than that. You don’t do this regularly, but every once in a while, once you’ve established that foundational, deep, authentic trust, you need to create space to go deep and discover the full human. I cannot state enough how important facilitation is. You really need to establish a space of safety, openness, and honesty if you want this to work. If you do that, you’re going to reap some beautiful rewards. The first one I want to talk about is the life graph. The idea here is everyone gets 10 minutes alone to draw a graph of their life, the peaks and the valleys, and everything in between. Then you come back together in small groups and you present your life over somewhere between 15 and 25 minutes. It sounds like a long time. Fifteen minutes is not a lot of time to talk about your life. This exercise, I love it for a few reasons. First, no one knows your life but you. You can control, you can omit pieces that you don’t want to talk about. You can also soften the delta between the peak and the valley to fit a story that you’re comfortable sharing. I guarantee you, you will be surprised by what people bring to the table, how open they’re willing to be. We don’t get a lot of opportunities in our life to share the depth of what we’ve been through. When you’re invited and you have that safety and that trust, it’s a really cathartic and humanizing moment together to share the history and the challenges and the surprises, that is behind the curtain, that is behind the human you see on a screen every day.

Life graph takes a long time, and it’s a pretty big investment. I think it’s worth it, but sometimes you need something a little bit more contained. The prompt I like is just tell the story of a formative moment in your life. I think you need to come up with a deeper prompt, but the idea is come up with these spicy, or risky, or evocative questions that you can each answer in small groups, and share. Something like, recount a challenging moment with your guardians. I think a lot of us, not everyone, but many have had a rich experience with their parents, with their guardians, with other people in their life. I pick the word challenging, because I want to dig into something that’s hard and memorable. Again, share something that doesn’t come up in normal conversation, to provide that real depth to who the human is and how they got there. The important thing with this is that you actually need all three of these. You need some surface level fun. You need a space to get into real conversation. You need the occasional moment where you really get to know someone. It’s important that you don’t only do any one of these. If you only have fun, it’s too shallow, it’s too surface. You’re never going to really build that foundation of trust. If you only ever have real talk or the full human stuff, it’s just too heavy. Sometimes you don’t want to talk about your baggage every single week, sometimes you just want to have some fun. That variety and variation keeps it fresh, and engaging, and exciting. It’s something that people can look forward to as a moment to be together. All of these things work together to build this beautiful tapestry of venues where folks can be themselves, can open up, and can build authentic human trust. Which brings me to my second main takeaway, fun, real talk, full human, mix it up. Create that space to build authentic and honest human trust.

A small segue into communication environments. In the world of digital, we really have three avenues available to us when we communicate: written, video, and in-person. Written is where we’re going to spend the vast majority of our time. Video will be depending on who you are, and your company, anywhere from constant to, few times a day to a few times a week. In-person, if you are a remote company is likely to be a few times a quarter to a few times a year. Each of these plays a different role. Written is where most of your communication is going to happen. It does help create clarity on hard problems. It forces us to distill our thoughts into something that can be consumed and read. Written is really susceptible to the emotion of the reader. It doesn’t work as well for sensitive or people-oriented conversation or topics because it’s just too hard to consistently capture that emotional nuance in written form.

Then we move to video. This does give us something closer to an in-person conversation, but there’s a few subtle and really critical differences to this particular medium that we find ourselves in. First, by definition, you’re basically in the person’s personal space. You are all with me right now inside my office. There’s a little bit of subconscious guard that comes with that. Second, physical cues are actually a lot harder to see and pick up on. People start to build this camera persona where we are more exaggerated, where our emotions are big, or where we make bigger gestures to try and convey some of what was subtle and easy to pick up before. That’s actually a lot more draining on people. In camera is actually a lot more tiring and very rarely an energizing activity. Lastly, video doesn’t scale in the same ways as in-person. Fundamentally, you’re not going to be able to have the same volume of humans engage in the same way in a video call as you can in-person. Lastly, even if you’re a fully digital company, actually think in-person is still absolutely necessary. At Shopify, we found two big angry moments where you really need these in-person things. One is a big deep dive on social connection. The other is important project moments where you can 10x something. On that social connection piece, there’s a lot of facilities and techniques we can do to build trust in a digital environment. You can accelerate that dramatically, with a day or two together in-person, having drinks, and activities, and sharing stories late into the night. Create that. Think about that.

Similarly, you can actually really build strong moments about your company’s culture and mission, in-person, that are hard to translate digitally. When we have our big social get-together, we break up by org at Shopify. The last one I was at, we had this beautiful merchant walk, where there was a whole bunch of live Shopify merchants selling goods that we were able to visit, buy stuff from, hear how they use the products, hear what’s hard about running a business, hear what’s exciting about running a business, and really get to experience firsthand their stories. That was a deeply connecting moment to the mission we have as a company. Again, you can recreate that with videos, but it’s never quite the same as like having that direct face-to-face conversation. With important project moments, I think there are three phases where you’re really going to 10x how something goes by doing it in-person. Key project kickoff, where you’re just launching a project for a couple months to several months. When you’re building an identity or strategy for an organization. When you’re taking those step backs, and having huge reflective moments for the year, the quarter, a big project. All of those are spaces where you can do certain kinds of activities that just work better in-person. Don’t shy away from that rare opportunity to get together. Let’s bring this back to our example. This morning convo is what we’re recreating in a few ways with the approaches outlined. The fun times help build initial trust and create space, the real talk and full human for depth and trust. The space creating with fun times actually helps allow Emily to talk about her personal passions. The trust that we’ve built gives Adrian safety to open up about the things that aren’t great, but actually want to step a layer before those trust building activities.

First Impressions Matter

If we really want these humans to flourish in our teams and our organization, we have to start even sooner. Which brings me to the next major topic, first impressions matter, aka onboarding. Onboarding has always played a critical role in culture setting. In remote environments, it steps up in essentiality. I really want to dig into why in remote environments onboarding is so critical. Again, it’s because everything requires intentionality. There’s a lot of ways you have to be more intentional. In digital, it’s a lot harder to ask for help. You just don’t know who to go to, when to go to them, how to go to them. As a lead, it’s a lot harder to notice when folks are lost. You just don’t have the same physical moments where you can see someone struggling, when you can stop by casually and do a very light check-in. Onboarding can provide the tools to make that better. We can set the stage for how to engage. We can help folks know when they can, when they should, when they must engage with folks. How to engage with them. Who to engage with. We can also help remove some of the ambiguity which goes a long way in digital communications going well.

Similarly, onboarding is a place where we can offer tools to navigate this digital sea. We can give a map, a compass to how to navigate through our crazy digital setup. That brings me to my third major takeaway, care more about onboarding, a lot more. This is such a high-leverage, high-impact moment that lays the foundation for everyone’s success. This is a really rare opportunity to establish and maintain the culture that you care about as a digital company. How do you do this well? Know what you want to accomplish. Establish the company culture, the mandate, the purpose, the mission. Set up the role. Set up the organization. Give perspective about how it fits and what it’s trying to do, what it’s trying to achieve. Lay the groundwork about the technical and system wayfinding, the technical tools you use, the systems and where they fit, and how they interact together. Where you can find more. Where you can play. In all of the ombre you run, seek balance. Being on camera all day is exhausting. Also, doing this all solo is deeply isolating, and not the welcome environment you want when someone is new to your company. Also, use this as an opportunity to foster community. Onboarding is a rare moment to build cross-org, cross-discipline groups in this digital world. It can provide people a thread to break out of their team, their org, their discipline bubbles, so don’t miss this. Network building is so hard in remote environments. That is my fourth main takeaway. Build those diverse connections. Do not miss this. This will create breadcrumbs that will help your org stay connected far into the future, well past this person’s onboarding.

To just ground some of this, I’m going to share Shopify’s onboarding, and specifically, our four-week onboarding program, which is crazy, but it’s so worth it. Broadly, it’s organized into cohorts. It’s mixed across disciplines and organizations to build those connections. It is a blend of facilitated, guest presentations, group work, and self-work. We’re trying to get that mix and that tapestry of variation, and different kinds of work to manage energy. This first week, getting to know the company, aka, Shopify startup. We share company context, and culture, and mission. This first couple days is 100% guided, with some end of day self-reflection activities and end of week social, to really cement that initial onboarding group together. It’s guided because we’re trying to give people something easy to enter into the company with, while also laying the framework for how the rest of this onboarding is going to go. We then transition to week two. We get a little bit closer to the ground, getting to know the product, aka, bridge the gap. This is still structured, but this is when variation of facilitated, self-driven, expert presentation, and social comes into play. Throughout this entire week, people mix between doing a product walkthrough, doing live support, and building their own shop using the products that we’re all responsible for in this company.

Week three, we get even closer to the ground, we start getting to know our craft, aka, Eng fundamentals, or UX fundamentals, or product manager fundamentals. This is all self and lead driven. Think, learning Ruby, how our infrastructure is deployed, developing on mobile, data analysis. The key with this is that the lead picks a few things to be the initial prompt, but the person going through this gets final say because they know what they know the best. They can pick and choose what is going to be useful to them. This is also when we finally expose people to the team Slack channels, and give them exposure to some but not all team rituals. We also start encouraging people to book one-on-ones and coffee chats with people on the team or in the organization, as through all three of these that we maintain that variation in the onboarding, where the formal onboarding is almost exclusively self-driven. The team rituals give a chance to go wide, and the one-on-ones give that deeper connection.

Then we get into the last week, getting to know your team, aka, Hello team, aka, when the person is done onboarding. We still call this onboarding because we want to keep a focus and a pressure on the person to stay attached to leveling themselves up. They’re more or less fully embedded in the team at this point. We start having people attend all product rituals. As an engineer, you’re going to be pairing pretty aggressively with your teammates to learn the nuances and quirks of our code. Throughout this week, and for the next one to three months, you’re going to get a slow drip of interesting and relevant async content. Ultimately, all of what you do this week, and really for the next couple months is guided by, use what’s useful, ignore the rest. We trust our people to be smart and do smart things, but we want to keep pressure here, again, to focus on leveling themselves up for long term success. People are hankering to start building by this point in time. Let’s go back to our example. Onboarding is what enabled you to have the conversations and the facilities and the spaces that you set up in that previous section. Onboarding also sets the tone. It makes it clear that you want people to show up as their authentic selves. That you want people to build high-trust relationships. That you value people dedicating time for fun activities. The onboarding sets the stage, so that you as a lead don’t have to do all of the heavy lifting yourself.

Effective Digital Conversations

To wrap everything up, I want to go into one last idea, effective digital conversations. All of the ideas we’ve talked about are essential, but they will not work if you don’t have good execution. I want to share a few tips and tricks, a few ways to have really good digital conversations. The first that I’m going to focus on here is the number of humans matters a lot. How many people are in your discussion, in your meeting, is going to dramatically change the way that you run it and how those people feel being in that meeting. One to four, pushing to five, you can have almost a natural conversation. You don’t have quite the same ebb and flow as you do in-person, but you get pretty close. As soon as you go above that 6-12, really 6-10, but you can push it to 12, everyone can participate. It’s not going to be a natural conversation, it has to be facilitated, but everyone can be an active contributor in the discussion that you’re having. As soon as you go above that to 13 or more, people are there to watch. They’re not active participants, a subset might be active participants. A group of 4 in your group of 13 might have a really active conversation, everyone else is just there to watch. Pay attention to that. Keep that in mind when you’re setting these up, and plan accordingly. I actually care about this so much that I think it’s often forgotten about. This is my fifth major takeaway, numbers matter. Be intentional with how many people you have present.

Back to some more tips and tricks. Be explicit. Have an agenda. Have a purpose. Have a goal. Have it written down. Share it with people in advance about why this meeting is happening, what you’re trying to achieve, what you hope to come to the end of the meeting with. You need to set that stage for there to be a productive and fruitful conversation. Similarly, be upfront that you’re going to be an active facilitator. You have to. Digital doesn’t work with as much of a natural ebb and flow, you need someone to play the role of making the meeting useful and effective. I actually find personally that I’m often really upfront, that I will interject, that I will overstep normal North American cultural bounds to keep the meeting on track. “Yes, Joe, the conversation you’re having about how Kafka is deployed is really interesting, but what we’re actually trying to solve today is why our deploys are always so slow. There’s a little bit of that that’s relevant, but we need to create space for Katherine to share what she was going to say.” You’d have to be unafraid of directing and redirecting the conversation. It’s just natural human behavior to fall into interesting rabbit holes, and you have to help guard against that.

This one, actually, I probably should have started with, but what are your rules of engagement? Do you even have rules of engagement? Are they published? Are they accessible? Are they known? This is a great thing to fit into your onboarding too. I’m going to share Shopify’s default, which we expect teams in orgs to refine and evolve for their context. These are really good basics. We want everyone to come in with cameras on, mics muted, or high-quality mics that don’t pick up background noise, with their name as their actual name. When we’re having a video call, we’re having it to be together in-person. If we didn’t want that it would be a written message. If you’re going to be there, be there. We want people to snooze notifications, and set their Slack status to say that they’re not going to be available, so that they’re not going to be pulled out of the meeting subtly. We want people to turn off their camera if something personal comes up. It’s not that we care if personal stuff comes up. Stuff happens. This is especially true in remote life. If it does, turn off your camera, step away. It’s just human nature to get distracted and curious when there’s something interesting happening in someone’s screen. This is a way to help everyone help themselves, stay where they should. Lastly, be on time, be engaged, be present. If you’re going to be in the meeting, be in the meeting.

Similarly, we’re in a digital meeting. There are some unique tools that come with being in a digital meeting, take advantage of that. Use the raise hands to manage who speaks, when. This is also actually really effective at balancing out power. I’ve found that you get a much more even spread of people who converse, when you’ve established the norm of raising your hand when you have something to say, or to add yourself to the queue versus always deferring to those with authority. Use breakout rooms to do focused work. When you have a bigger group, you can recreate moments of small groups to have that rich conversation that gets brought back. This also helps minimize overhead of having to switch between multiple meetings to get that same effect. Take advantage of your camera on and off, music, and timers. These are all tools you can use to manage expectations. Have the camera off when you’re doing individual work and back on when you’re done. Start meetings that are bigger with music to fill awkward silence so that people don’t feel like, what do I do when there’s 40 people but nothing’s being said? Have timers visible when there’s activities happening so that people know how much time they have. They can manage themselves. They can know what’s to come and not be surprised. Similarly, play with digital whiteboards and a whole bunch of other digital tools. Really, this pairs well with the last two points, but take the advantage of other shared digital venues you can use to make the meeting, to make the work you’re doing together more effective.

Lastly, I want to dive into managing energy. People might be spending literally 8 hours nonstop on meetings and you won’t know. People sometimes struggle to disconnect. We don’t have the walking between offices anymore to force a small break. It goes on to the onus of the facilitator to make sure this happens. I think it’s really important in certain scenarios. For example, if you’re running a 2-hour plus meeting, you better have some breaks in there. This helps you just as much as it helps the others. If people are sitting for too long, they’re not going to stay engaged, you’re going to lose them. Having that break is going to help them disconnect and refresh. It’s also going to help them stay present and bring their best selves to your discussion. If it’s a recurring/standing meeting, make a habit in its structure and flow. For example, I have a weekly senior leads meeting with all of our discipline leads, that the first 10 minutes of that meeting is always cameras off, updating an async doc and commenting on it. Then after the 10 minutes, we all turn our cameras on, and we dive into relevant, interesting conversation that comes out of that work. It’s always set up that way. I do that for two reasons. One, actually, I need people to update that doc, because it drives the best conversation if we do that asynchronously. Two, I know all of my leads are slammed in meetings all day. This is a good way for me to sneak a break in for them so that they can go to the washroom, they can stretch, they can get a fresh coffee, whatever they need to survive the day. Lastly, create moments of movement, if you can. This gets back to one of the first points I brought up about creating fun. If you have regular team meetings, you can fold in fun into them as well. One of my teams, they don’t do this every sprint planning, but every couple weeks, they’ll throw in a very light touch social moment to start off the meeting. Share a 30-second story of something meaningful on your desk. The first person to bring back a spatula from their house, demos first. They are little things, but they create these moments of excitement, these moments of joy, these moments of movement that really elevate the conversation. All of this, what I’m really getting at here is you have to facilitate. You just can’t take a passive role in digital meetings, you need to be actively engaged, if you want to get good stuff out of them, have good experiences for everyone involved.

Let’s do one last visit to Adrian and Emily. This is a group of four. We could actually recreate this almost exact morning chat, have a coffee chat in the morning that’s available for everyone to hop into from 8:30 to 9:00, when people have their morning coffee. This is only going to work if you set expectations that it is a social chat that happens on these timeframes for us to shoot the shit. It’s only going to work if numbers stay small enough, that number slide, if it’s really under five people, or more people that are willing to be passive and just watch. You really do have to establish the norms with onboarding, and have built enough trust that people are willing to jump into something like this. Even in a casual conversation like this, there’s still a role for facilitation. It’s just going to be subtle. You might want to redirect the conversation at some point to someone who’s been quiet, and you might want to notice if someone’s hogging all of the air and kindly redirect it somewhere else. Really, facilitation plays a role in everything that leads into being able to have this venue.

Key Takeaways

We’ve covered four broad areas: intentionality, a focus on trust, how important first impressions are, and effective digital conversations. There’s six key takeaways that I really want you to bring back with you. First, trust as humans. This is the rock we all stand on, and creates flex for mistakes. Two, fun, real talk, full human, and mix it up. This is how you build that deep personal trust, that is the foundation and groundwork to high-performing teams, to happy teams. Three, care more about onboarding. This is such a high-leverage, high-impact moment. You would be crazy to not heavily invest in what you do in this space. Establish what matters, and set up the structure for your folks to succeed. Four, build diverse connections. Onboarding is a rare moment to build networks outside of our natural team bubbles. Don’t miss that. Help folks build those dots across the org so that you, as your org grows, has more natural connections all over the place, that’ll make you all stronger. Five, numbers matter. Be hyper-intentional. This is one that is often forgotten about in digital meetings. Digital is just different than in-person. We really need to change how we make the space available, how we set it up, based on the number of people we have present. This is all powered by good facilitation. You have to facilitate, if you want any of this to really work. Really, what it’s all about, be intentional.

Resources

If you liked this, if you have more thoughts, reach out, engage with me. On my website, jcmcginnis.com, I write about stuff like this in my own journey of people craft in the tech industry. I have a short book called “Embracing Uncertainty” which I would call a field guide to scrum that was written in the before times, all very office centric. I think it’s still pretty applicable to the digital world.

Questions and Answers

Stanier: Do you have any suggestions for working with contractors who have come with the assumption that they’re expected to “just close tickets?”

McGinnis: The little add that he added there of like, assuming that we don’t actually want this? It depends on what you want out of your contractors. I think a lot of this, to me, just comes down to, what are the expectations you set up front when you bring the contractor in? How you establish that baseline is going to hugely influence how open they might be to being engaged in or involved in some of those other activities that you can do with your “in-house team.” I think you’re going to get a bit of a different flavor because they’re not going to have the same maybe onboarding opportunity or the same cultural touchstones. Also, a lot of that is also in the control of the company in how they choose to engage with contractors. I can think of one contractor I’ve had, in my time at Shopify, where our mode of operating has generally been to treat contractors as if they’re full-time employees, they just happen to be entered into our organization through a different contractual lens. Under that light, it has not been hard at all to deeply engage them. Similarly, though, I’ve talked to friends in other places where that’s not the tone that’s set when the contractor is engaged. Once that initial tone is set, it takes a lot longer to open them up to the idea that this particular team wants to have a richer connection with them. You have to play it by ear.

Stanier: My own peer group of work, I love the real talk idea. It’s amazing. I would feel super awkward bringing that up with that particular peer group. Do you have any tips for getting real talk in resistant groups?

McGinnis: There’s a few different flavors of ways that you can approach it. The first time I ever did this was a terrible joke, we talked about monthly trust falls with a group of about 40 people, and this was in-person. We just slowly escalated the intensity of thing that we did that was always paired with something lighthearted, something medium, something heavy, and then each month, we got into a richer version of each of those, and so warmed the waters to it. In my most recent change inside Shopify, when I came into a new org maybe a little over a year ago, when I was establishing my area, or my Eng leads group, I actually led immediately with like, all of you are going to think this is weird, you’re all going to find this uncomfortable, I want to be up front that you do not have to participate. I set like, basically, really carefully managed the space, the facilitation, and shared stories of people that had had turnaround experiences. Then, everyone DM’d me afterwards of like, “I thought that was going to be the worst, but I tried it and it was actually way better than I thought it was.” Then everyone similarly, a couple weeks later was like, “No, you’re actually completely right. I thought you were pulling our teeth. It really did work, and now I want to figure out how to do this with my team. What are some of those facilitation tips,” or stuff like that? Of all the things I talked about, I think the life graph is the one that takes the most set up, but you can start with something a little bit softer before you get to that intense. Even on that one, there’s a peer group that I’m involved with in Toronto, where part of their training to be part of the program, you have to do the life graph with a whole bunch of strangers. That’s just part of what gets you entry into the peer group, because the whole point is founders sharing the dirty secrets of their companies. You have to trust the people that you’re talking to. Because of that context, the idea of sharing something personal wasn’t a burden that couldn’t be passed.

Stanier: Did you get a chance to implement no-meeting days? How did it go?

McGinnis: Tobi did this, originally rolled out without announcement. We had a bot that auto-deleted all of our events on Wednesdays, and then the bot reactivates every couple years, and re-clears out the Wednesdays. Pros and cons. I think, as a whole, it makes a lot of sense in that, at least in Canada in particular, we’re sensitive, there’s a lot of social norms that you fall into. These resets let you take a moment of intentionality of like, I have all of these meetings that are standing, do I actually need them still? Are they doing what I meant them to do? I don’t know if I would go as far as to like, immediately, without announcement, delete everyone’s events, because there was a lot of stuff that got hit in the crossfire that was really important. On the whole, it probably worked out. Even today, all of my Wednesdays are not meeting-free, but I do find that my block on my calendar says this is a meeting-free day, at least gives me a good nudge of if someone does book something, I can DM them and be like, is this really important? Is this the right thing to have today? Can we move it to this other day? People are much more open to it. I think it’s a good tactic at protecting makerspace, especially for those of us who don’t get a lot of space in our calendars.

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Fauna, which offers a distributed document-relational database service, on Tuesday introduced a new version of its Fauna Query Language, promising a better developer experience with less database code.

FQL is not to be confused with Microsoft’s FAST Query Language, Postman’s Flows Query Language, Falcon Query Language, Facebook Query Language, Filter Query Language, Feature Query Language, or other F-encumbered query languages that can be similarly abbreviated.

“The founding team started getting going on this in 2016 and was working on the initial architecture and the initial version of FQL,” explained CEO Eric Berg in an interview with The Register.

The team, he said, had been dealing with the challenge of scaling Twitter – co-founders Matt Freels and Evan Weaver served as the technical lead of Twitter’s database team and director of infrastructure of Twitter respectively.

They created the initial version of Fauna and FQL, said Berg, which offers distributed document-relational capabilities with strong consistency across multiple regions that scales easily.

“The language that they created, the first version of FQL, they basically optimized it for power users,” Berg explained, citing as an example how indexes were required to interact with everything because that’s the optimal way to work with databases at scale. “Some developers really loved it because they loved the control and power it would give them. But it was also very verbose and it had some constraints.”

The goal with the new version of FQL, said Berg, is to open up the underlying architecture of Fauna to developers while making it more familiar and capable. So the Fauna team took inspiration from modern programming languages like TypeScript and Python and from GraphQL, a query language for APIs.

“The [FQL] syntax is very familiar and inspired by TypeScript and Python,” said Berg. “So you get that familiarity, but you also get all the logic constructs that you get in a modern programming language… that you don’t get in SQL.”

SQL, which will be 50 years old next year, stands for Structured Query Language as almost anyone in the IT business will tell you. It’s the standard language for managing data in relational databases.

The folks at Fauna describe their database service – which you access via API instead of through software that you install on a server – as a “NoSQL database that has the relational capabilities of [a relational database management system].”

It’s positioned as an alternative to AWS’s managed NoSQL database service DynamoDB. But Fauna also sees itself competing with MongoDB, Postgres, and “serverless” AWS Aurora, among others.

“We also take inspiration from GraphQL,” said Berg. “So that you can do projections of your data. You can basically manipulate the data and bring it back to your application in the format that it needs to fit into – however your UI consumes that data. And you do that natively in Fauna. Whereas in SQL, because all the data is stored in tables and rows and columns, you have to go through some sort of translation layer, usually what’s called ORM (object relational mapping).”

Fauna, he said, has a sort of native ORM that does better in terms of performance and efficiency.

Fauna is also adding support for a declarative database schema. “With Fauna, you can start in an unstructured mode and then you can add that schema over time to structure and control your data,” said Berg, who observed that this helps Fauna place nicely with DevOps tooling.

Berg said Fauna has more than 350 paying enterprise customers, in addition to more than 3,000 teams actively developing applications with Fauna that may evolve beyond the free tier.

Pointing to the service’s new web shell, command line interface, administrative dashboard, intelligent code completion and extension for Microsoft Visual Studio Code, Berg said: “We’ve really made sure that, along with the familiarity from a language perspective, the development experience that you have is also leveraging best in class software development practices.” ®

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Large Model Systems Organization (LMSYS Org) recently released Chatbot Arena, a comparison platform for large language models (LLMs), where users can pick the better response from a pair of chatbots. LMSYS also released a dataset containing conversations from the Arena as well as a dataset of human annotations of results from evaluating LLMs on the MT-Bench benchmark.

LMSYS Org created Chatbot Arena earlier this year to “crowdsource” an evaluation of several different open- and closed-source LLMs, including GPT-4 and LLaMA. The Arena produced a leaderboard of models, ranking them according to their Elo rating. Because this method was time-consuming, the LMSYS team developed an additional benchmark, MT-bench, which consists of 80 multi-turn questions to ask a chatbot, with the chatbot’s responses graded by GPT-4. According to LMSYS Org: 

[We] have shown that MT-Bench effectively differentiates between chatbots of varying capabilities. It’s scalable, offers valuable insights with category breakdowns, and provides explainability for human judges to verify. However, LLM judges should be used carefully. It can still make errors, especially when grading math/reasoning questions.

The rise of LLMs has led to a need for new benchmarks to measure their abilities, as the models have achieved superhuman performance on traditional ones like GLUE. The Massive Multitask Language Understanding (MMLU) benchmark can measure a LLM’s knowledge capabilities, but it does not measure how well the LLM can produce output that is aligned with human preference, which is the feature that new models such as ChatGPT are pursuing.

Earlier this year, LMSYS Org released their Vicuna LLM, a fine-tuned version of Meta’s LLaMA model. To evaluate Vicuna, the researchers used GPT-4 as a judge of its output, and claimed that Vicuna achieved “more than 90% quality” of ChatGPT and Bard. Within a few months, LMSYS Org announced the ChatBot Arena, as an attempt to crowdsource the evaluation of models. Users would interact with two different models at once and choose which one they preferred; the result is an Elo rating of models. In this latest move, LMSYS Org is releasing a dataset of 33K Arena chatbot conversations with humans.

After running the Arena for several months, the researchers identified 8 categories of user prompts, including math, reasoning, and STEM knowledge. They created 10 multi-turn questions for each category, producing MT-Bench, a “quality-controlled complement” to the Arena. They again used GPT-4 to grade a chatbot’s responses to the benchmark prompts, and found that the GPT-4 judge agreed with human judges more than 80% of the time, which was similar to how often two different human judges agreed. GPT-4’s explanations for its choice could even persuade human judges to change their picks 34% of the time. LMSYS Org has now released a dataset of 3.3k “expert-level pairwise human preferences” for responses generated by six different models.

ML researcher Nathan Lambert discussed the work on Twitter, pointing out that the MT-Bench score “seems like the clearest benchmark to optimize” for researchers trying to produce models that match leaders like GPT-4. MT-Bench co-author Wei-Lin Chiang also answered several user questions on Twitter. In response to a question about correctly using models when evaluating them, Chiang replied:

That’s a great point. We try our best to find the official template if it exists…But lack of standard and LLM’s sensitivity to the template is definitely an issue.

The Chatbot Arena and MT-Bench evaluation code are available on GitHub. The Arena conversation dataset and MT-Bench response dataset are available on Huggingface, as is the current LLM Leaderboard.

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AWS announced the preview release of vector storage and search capability within Amazon OpenSearch Serverless. The capability is intended to support machine learning augmented search experiences and generative AI applications.

Amazon OpenSearch Serverless is the serverless offering of the Amazon OpenSearch Service. Based on the open-source search project Apache Lucene, Amazon OpenSearch enables ingestion, storage, search and analytics across JSON documents.

Historically, search in Lucene-based systems has relied on keyword matching along with a scoring metric to control the relevance of results. Keyword matching works by building an inverted index of terms, linking them to documents in which they appear. Ranking results, on the other hand, works by converting individual documents into a vector within a sparse N-dimensional space, where the weights for each dimension are outputs from the scoring metric and the dimensions are the terms across all considered documents. This approach, often referred to as lexical search, while efficient can exclude documents that semantically answer a query but omit key terms from it. Vector Search is a newer approach that aims to overcome this limitation.

With Vector Search, a query is converted into a dense vector via a transformer model and results are found by identifying documents with similar vector representations.

An illustration of how text might be represented as a vector embedding (Source: AWS News Blog Post)

This requires a storage system that can appropriately index these vectors, referred to as embeddings, and identify similarity by a search for neighboring vectors. This functionality is what the Vector Engine for Amazon OpenSearch Serverless aims to provide.

With the new engine, Vector Indexes for embeddings of up to 16,000 dimensions can now be created along with a similarity metric of Euclidean, Cosine or Dot-Product. In addition to the vector field, the new indexes allow each document to have up to 1000 fields as contextual metadata for search filters.

Screenshot of the vector index creation page on the AWS Console (Source: AWS News Blog Post)

By taking advantage of the OpenSearch Serverless architecture, the vector engine removes the need for users to implement sizing, tuning or scaling controls on the underlying infrastructure. It also supports the OpenSearch suite APIs which allows the existing lexical search, filtering, aggregation and geospatial query functionalities to be used in tandem with vector search.

Screenshot of an OpenSearch query combining vector and lexical search approaches (Source: AWS News Blog Post)

A drawback of the tight integration with OpenSearch Serverless is that use of the vector engine is subject to the same minimum requirement of 4 OpenSearch Compute Units (OCUs) as other workloads. Feedback from the community on Amazon Opensearch Serverless has primarily been focused on its relatively high costs, with a user ralusek commenting on the r/aws thread:

” … it seems as though we’re looking at a $700/mo minimum fee . . . for tinkering with projects, this just seems absurdly high . . . “

It would appear the Amazon Opensearch Serverless team have taken onboard the user pain points as the announcement included future plans to have a 1 OCU minimum requirement. The team have also offered 1400 OCU-hours per month for free to enable customer experimentation prior to general availability of the service.

For similar generative AI and machine-learning workloads, alternatives to the Vector Engine for Amazon Opensearch Serverless would be PostgreSQL with the pgvector extension, Elasticsearch Vector Search and Pinecone.

Finally, further information on how to get started with the Vector Engine for Amazon OpenSearch Serverless can be found on its documentation page.

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Recently Golem released its flagship product Golem Cloud, a durable computing platform allowing developers to build and deploy long-running, stateful serverless workers that are resistant to failures, upgrades, and updates. The product is currently in developer preview.

Golem Cloud is built on WebAssembly (WASM) with the component model. Any WebAssembly component deployed on the Golem Cloud will be executed durably until the component is done. Developers can compile their project (written in languages like Rust, Zig, Go, Grain, C/C++ that target WASM) into a WASM Component and upload it using the Golem CLI or the Golem REST API into Golem Cloud. Subsequently, Golem prepares the component for fast instantiation and assigns the component a globally unique identifier, which developers can use for creating new running instances of the component.

Example of a worker (Source: Golem Cloud Website)

Under the hood, the platform will continuously persist each serverless worker’s running state and transfer this state to new machines as necessary in response to hardware failures, hot spots, upgrades, and updates. The company coined the term “invincible” for workers deployed on the platform.

John A de Goes, an OSS contributor, explains in a Golem Cloud blog post:

The nodes that run your workers are continuously snapshotting the state of workers. Not just their heap memory but also their stack memory, and including the so-called instruction pointer, which determines where in some procedure your code is executing at the current moment in time. A full snapshot of everything.

In addition, he explains the rationale for the snapshots to allow recovery after failure:

These continuous snapshots are not whole-memory snapshots, because that would reduce performance and increase latency. Instead, the snapshots are implemented through capturing tiny incremental deltas, which permits reconstructing the full state during recovery, while keeping performance high and latency low.

Running serverless workers such as functions is also possible in, for instance, Microsoft Azure with Durable Functions that includes a runtime to execute workflows and actors. However, under the hood, durability is provided through Azure storage containers, tables, and queues with the option to change to Netherite. In addition, AWS offers Step functions to execute workflows with built-in state management. Both differ from the Golem Cloud in respect of the WebAssembly component model.

However, there is a comparable offering from Fermyon Technologies, who earlier this year launched Fermyon Cloud as the way to deploy and manage cloud-native WebAssembly applications through their developer tool, Spin.

In a Serverless Daily post, the authors wrote:

Golem Cloud is an interesting new serverless platform that has popped up this week that takes a different approach to most platforms.

In addition, a respondent on a Hacker News thread commented:

The paradigm they are going for seems less like scale-to-zero and more like sure-wake or never-fail or infini-migrate workers, and that bodes well for being able to be charged according to the exact amount of active ram that your worker uses moment to moment rather than per invocation on an inflexible amount of ram.

Lastly, Golem Cloud currently offers a free preview plan that includes ten active workers, 10,000 inactive workers, and 10 MB per worker – other plans are coming soon.

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Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Someone with a lot of money to spend has taken a bullish stance on MongoDB MDB.

And retail traders should know.

We noticed this today when the big position showed up on publicly available options history that we track here at Benzinga.

Whether this is an institution or just a wealthy individual, we don’t know. But when something this big happens with MDB, it often means somebody knows something is about to happen.

So how do we know what this whale just did?

Today, Benzinga‘s options scanner spotted 12 uncommon options trades for MongoDB.

This isn’t normal.

The overall sentiment of these big-money traders is split between 50% bullish and 50%, bearish.

Out of all of the special options we uncovered, 4 are puts, for a total amount of $167,511, and 8 are calls, for a total amount of $297,474.

What’s The Price Target?

Taking into account the Volume and Open Interest on these contracts, it appears that whales have been targeting a price range from $250.0 to $620.0 for MongoDB over the last 3 months.

Volume & Open Interest Development

Looking at the volume and open interest is an insightful way to conduct due diligence on a stock.

This data can help you track the liquidity and interest for MongoDB’s options for a given strike price.

Below, we can observe the evolution of the volume and open interest of calls and puts, respectively, for all of MongoDB’s whale activity within a strike price range from $250.0 to $620.0 in the last 30 days.

MongoDB Option Volume And Open Interest Over Last 30 Days

Biggest Options Spotted:

Where Is MongoDB Standing Right Now?

• With a volume of 306,775, the price of MDB is up 2.07% at $358.1.
• RSI indicators hint that the underlying stock may be approaching oversold.
• Next earnings are expected to be released in 10 days.

What The Experts Say On MongoDB:

• JMP Securities has decided to maintain their Outperform rating on MongoDB, which currently sits at a price target of $425.

Options are a riskier asset compared to just trading the stock, but they have higher profit potential. Serious options traders manage this risk by educating themselves daily, scaling in and out of trades, following more than one indicator, and following the markets closely.

If you want to stay updated on the latest options trades for MongoDB, Benzinga Pro gives you real-time options trades alerts.

Article originally posted on mongodb google news. Visit mongodb google news