Article originally posted on InfoQ. Visit InfoQ

A research team from Baidu published a paper on the 3.0 version of Enhanced Language RepresentatioN with Informative Entities (ERNIE), a natural language processing (NLP) deep-learning model. The model contains 10B parameters and achieved a new state-of-the-art score on the SuperGLUE benchmark, outperforming the human baseline score.

The model and several experiments were described in a post on Baidu’s blog. Unlike most other deep-learning NLP models that are trained only on unstructured text, ERNIE’s training data includes structured knowledge graph data, which helps the model output more coherent responses. The model consists of a Transformer-XL “backbone” to encode the input to a latent representation, along with two separate decoder networks: one for natural language understanding (NLU) and another for natural language generation (NLG). In addition to setting a new top score on SuperGLUE, displacing Microsoft and Google, ERNIE also set new state-of-the-art scores on 54 Chinese-language NLP tasks.

Although large deep-learning models trained only on text, such as OpenAI’s GPT-3 or Google’s T5, perform well on a wide variety of problems, researchers have found these models often struggle with some NLU tasks that require world knowledge not present in the input text. To address this, in early 2019 researchers at Tsinghua University open-sourced the first version of ERNIE, a model combining text and knowledge graph data; later that year, Baidu released the 2.0 version, which was the first model to score higher than 90 on the GLUE benchmark.

Like GPT-3 and other models, ERNIE 3.0 is pre-trained on text using several unsupervised-learning tasks, including masking and language modeling. To incorporate knowledge graph data into the training process, the Baidu team created a new pre-training task called universal knowledge-text prediction (UKTP). In this task, the model is given a sentence from an encyclopedia along with a knowledge graph representation of the sentence, with part of the data randomly masked; the model must then predict the correct value for the masked data. Overall, the training dataset was 4TB, the largest Chinese text corpus to date, according to Baidu.

The researchers evaluated ERNIE’s performance on several downstream tasks. For NLU, the team fine-tuned the model on 45 different datasets for 14 tasks, including sentiment analysis, news classification, named-entity recognition, and document retrieval; for NLG, 9 datasets and 7 tasks, including text summarization, closed-book question answering, machine translation, and dialogue generation. On all tasks, ERNIE set new state-of-the-art performance scores. To measure zero-shot NLG performance, human annotators were asked to score the output from ERNIE and three other models. According to these results, ERNIE generated “the most coherent, fluent and accurate texts on average.”

Neural-symbolic computing, the combination of deep-learning neural network models with “good old-fashioned AI” techniques, is an active research area. In 2020, a team from Tsinghua worked with researchers in Canada to produce KEPLER, which was trained on the text content of Wikipedia combined with the structured Wikidata knowledge base. More recently, a team at MIT combined a GPT-3 deep-learning model with a symbolic world state model to improve the coherence of GPT-3’s text generation, and researchers from Berkeley have combined a neural question-answering system with a “classic AI” crossword-puzzle solver called Dr. Fill.

Although Baidu has not released the code and models for ERNIE 3.0, version 2.0 is available on GitHub. There is also an interactive demo of ERNIE 3.0 on Baidu’s website.

Article originally posted on InfoQ. Visit InfoQ

The CNCF-graduated project Envoy Proxy was recently announced as generally available on Windows. Starting with version 1.18.3, engineers can use the Envoy proxy on Windows for production workloads.

Sotiris Nanopoulos, Engineer at Microsoft, said that the Envoy-Windows-Development group worked together to get Envoy on Windows over the last year. Thanking the Envoy developer community and maintainers, Nanopoulos stated that post the alpha release in October 2020, there is improved performance and reliability of Envoy.

To get started, operators and developers can utilize the container images on the Docker Hub. These lightweight images are equipped with various SDKs and tools in order to experiment with Envoy.

With a lack of support for edge-triggered change notifications on Windows Server 2019, Envoy on Windows ended up draining CPU resources. Version 1.18.3 supports synthetic edge events, which behave like edge events. As inferred from the integration tests, which uses synthetic edge events, there is a considerable improvement in the polling mechanism. This allows scaling for multiple concurrent connections.

Stream access loggers enable operators to redirect access logs from listeners and the admin portal to the process’s standard output. This will improve diagnostics when running Envoy on different platforms with the same configuration. Additionally, to provide ease for developers and Windows native operators, Envoy process management has been enhanced since the alpha release. Envoy terminates gracefully when Ctrl+C and Ctrl+Break commands are sent to the console. Experimental support for Envoy as a Windows Service is also available.

During the last year, the contribution from the Windows Development Group consisted of 189 patches to the Envoy repository. Supporting two compilers , three runtimes (win32 native, SCM, and containers), and multiple versions of Windows, the group recently released the latest version, 1.19.0. Readers interested in detailed statistics can head over to this dashboard.

Envoy for Windows comes with support for Clang on Windows. From January 2021, for every commit, envoy.exe is built with both Clang and MSVC compilers.

Commenting about the future, Nanopoulos said that the group is looking forward to improving the distribution of binaries, performance and integration with Service Mesh solutions like Open Service Mesh for the upcoming Windows Server 2022 release.

Readers can get involved by joining the Envoy Slack Workspace and reaching out to contributors in the #envoy-windows-dev channel. In addition to the FAQ on the documentation website, GitHub issues are monitored, alongside discussions in envoy-dev and envoy-announce Google groups.

As a side note, the 4th annual EnvoyCon is happening on October 11th alongside KubeCon NA 2021. The schedule for the event will be announced on August 25th, 2021.

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Posted on mongodb google news. Visit mongodb google news

Many people hesitate making a career change in the interest of waiting for the right time, but in Austin’s tech scene, it seems the best time is always now.

Over the past year, more than 150 companies either moved or expanded their base of operations within the city limits, leaving opportunities abundant and demand for jobseekers high. Tech companies are also increasingly aligning their values and growth aspirations with the work they take on, and it’s important that the people who make up their teams have similar mindsets.

“Our mission to give people tools to save money and build credit was something I personally identified with as my family struggled with rebuilding credit growing up,” Ale Fautsch, a lead software engineer at Self, said.

Built In Austin sat down with employees at nine of the fastest-growing companies in the Silicon Hills city to discuss what drew them to their current role and what exciting possibilities are on the horizon.

Meg Rodriguez

Customer Development Director

QuotaPath automatically tracks quota progress and commissions in real time and allows for personal customization to keep sales teams motivated.

Earning what’s due: “What excited me most about joining QuotaPath was the opportunity to help solve a problem that’s very personal to me as a salesperson — compensation,” Rodriguez said. “Most sales reps today still have little to no visibility into their commissions, and don’t even fully comprehend their compensation plans. QuotaPath makes compensation easy to understand, automated and motivating by helping sales teams identify how they can be earning more. On top of that, the entire QuotaPath team brings experience, excellence and a ‘can-do’ attitude, which makes every day at work rewarding and motivating. Our new office in East Austin features a neon sign that says ‘good vibes,’ and the QuotaPath team truly brings that feeling all around — to each other, to our customers and to the market.”

Qualifying user experience: “I’m most excited about putting sales compensation software — and QuotaPath specifically — at the top of every company’s sales tech stack. I have the opportunity to help do that by leading our customer success and account management teams to ensure our customers have an amazing experience working with our team and product, and that we’re helping them to solve their unique business needs. We’re on a mission to help companies have fewer spreadsheets and more time back to close sales.”

Ale Fautsch

Lead Software Engineer

Self helps underserved people save money and build and establish their credit.

Finding personal connection: “I wanted to work at Self for one primary reason: impact,” Fautsch said. “Our mission to give people tools to save money and build credit was something I personally identified with as my family struggled with rebuilding credit growing up. I was given the opportunity and resources at Self to build data infrastructure from the ground up, and I believe my work helped directly impact our customers in a hugely positive way.”

Expanding product impact: “We’ve been trusted by millions of customers to guide them toward financial wellness. Using our talented people and the power of data, I’m excited to help build even more personalized products and tools to help our customers reach their goals.”

Kasey Carroll

Director of University Relations

REX’s digital platform and full-service real estate brokerage uses artificial intelligence and machine learning to identify buyers and sell homes outside of traditional multiple listing systems.

Seeing a clear future: “For me, the deciding factor was the potential for growth at Rex, both for myself and the company as a whole,” Carroll said. “It was obvious that joining this team would mean being a part of building something, and I was invigorated by the idea of what that impact could be. In my interview process, I could see that there was a collaborative spirit that allowed people to learn about all aspects of the company, and that there would be room to move around if I ever wanted to find a different fit for my skill set. As someone coming from a completely different field of work, this was comforting to me, and I knew that this was a team that prioritized keeping their people happy and challenged. The deal was sealed by how positive, hardworking and motivating every person I met on the team was. That remains true to this day.”

Growing the next generation: “There is so much possibility ahead of us right now at Rex. Specifically for me as director of university relations, I am so excited to help recruit the next generation of talent for our company. After spending years working in higher education, I know first hand that this generation of students is not only extremely intelligent and hardworking, but thoughtful, well-rounded, empathetic and motivated. The interns we recruit are going to have a massive impact on our company, both from a technical perspective and culturally as well. I can’t wait to work with them, and I can’t wait to make their experience here as fun and meaningful as possible. I have no doubt that the future of Rex will include being one of the top internship programs, and I look forward to working with my team to make that happen.”

Tyler McDonnell

Data Science Manager

SparkCognition catalyzes growth with artificial intelligence and machine learning technology to help clients adapt to a changing digital landscape.

Southern possibilities: “I joined SparkCognition as an intern while in the midst of completing my Ph.D. in Computer Science at The University of Texas at Austin,” McDonnell said. “What first drew me to the company was the nature of the work — forward-looking research in the field of AI is hard to come by outside of the West Coast. But I came to find that the research project I was working on at the time was only the first small dose of the broader culture of innovation and opportunity at SparkCognition. I ultimately decided to put my graduate studies on hold and join SparkCognition full-time. I’m glad that I did: I still get to learn every day from professors and leaders in the field of AI, but I also get to feel and see the real-world impact of my work.”

Expanding accessibility: “What excites me most is creating impact at scale. As long as I have been at SparkCognition, we have been working with some of the largest Fortune 500 companies, applying cutting-edge AI to solve some of the most difficult problems in the industry.

“When I look at how we can increase our impact as a company, the answer is to make AI as accessible as possible to as many people as possible to solve as many problems as possible. I see that reflected every day in our work at SparkCognition. We frequently develop novel AI technologies to solve real-world problems, and while that’s an interesting challenge in itself, it’s just as exciting — and oftentimes just as difficult — to generalize those solutions. We strive to extract the core components of our AI and make them easy to understand and intuitive to use so that our customers can use them to solve new problems, especially those we hadn’t thought of initially. That’s a difficult task, but that’s the scale and the impact that we’re chasing.”

Aaron Klaser

Senior Software Engineer

Identity platform CLEAR transforms fingerprints and face recognition into a secure, biometric customer experience in nearly 40 U.S. airports and venues.

Real-world impact: “Watching my code being used by tens of thousands of people every day is an exciting and rewarding experience,” Klaser said. “I have watched the company go from kiosks in the airport to helping open the world back up after COVID-19 lockdowns. What we have accomplished as a company thus far is something most people dream of getting to be a part of, and I am so excited to be a part of it.

“Due to our rapid growth, there are so many good problems to solve. CLEAR is very open to innovation, even outside our bi-annual hackathons. If you have an idea or a proof of concept, anyone from your peers to the senior leadership team is ready and willing to listen. I have never worked somewhere where my ideas are as valued as they are here at CLEAR. There is so much potential to be impactful.

“At the end of the day it comes down to the people and the leadership. It’s very rare to find a company that truly cares about their employees the way CLEAR does. From free meals to our CEO forgoing her 2020 salary to set up the One CLEAR Fund to financially support field employees impacted by COVID, I am proud to be on her team!”

Personal evolution potential: “It’s extremely exciting to see all the growth. There are new faces at every happy hour. CLEAR is in this exciting time where we are making the transition from a small company to a large company. The concerns began to shift from moving quickly to get features out the door to processes and scalability. These are good problems to have, but more importantly, they leave so much room for employees to step up and grow into a desired role. For example, I have been working on a server that would allow us to deploy UI code significantly faster. It’s great to have an idea and be given the opportunity and support to lead something from a simple documented idea to a demo of a proof of concept.”

Tracy Catlin

Contractor Account Executive

Billd is a payment and finance solution for the construction industry, allowing organizations to acquire the materials they need upfront and pay suppliers later.

Believing in the mission: “One of the biggest reasons I have been at Billd so long is because I believe in what we are doing,” Catlin said. “We are trying to solve a problem and provide a solution that makes way too much sense in the construction space. With different ideas and a new way of thinking, there are always going to be early adopters and a bit of an uphill battle in spreading the word about a new solution, but I’ve seen our product help hundreds of contractors grow their businesses. I wouldn’t be at Billd if I didn’t believe in what we were doing and the why behind our product. Also, the leadership, professional growth and surrounding talent all make for pretty compelling reasons to make yourself at home among the Billd work family.”

Customer-centric solutions: “I wake up every day excited about the future ahead for Billd. Billd has developed a new idea, product and way of thinking in an otherwise broken payment cycle within construction. I might be biased, but I believe I have one of the best roles at Billd in that I have the privilege of speaking to our customers, new and existing, day-in and day-out. I hear the struggles and pain points our customers face while running their businesses, and I am able to provide a solution where frankly there hasn’t been one before. I am excited about the partnerships we have in place and every conversation I have with a new customer knowing that we will help them take control and empower their business. But I am most excited that we are truly just beginning as a young company. The growth and future ahead for Billd, our customers and the construction payment chain is as bright as ever to anyone willing to listen.”

Charlotte Sullivan

Corporate Account Executive

MongoDB offers a document database management system on which developers can build and and applications can run.

Creating immediate connections: “MongoDB’s interview process really wowed me,” Sullivan said. “I found they spent less time selling me on MongoDB and more on uncovering what mattered to me and how I wanted to shape my career. Instead of pitching to me, they listened to me. The rigorous interview process showed me that they care deeply about the caliber of their candidates, and it inspired me to work harder to meet their standards of excellence. In my interview they told me that they would shape the talent that I already possessed, and that’s a promise they’ve delivered on. MongoDB is meticulous in its hiring so that the company can enter into mutually-beneficial relationships with its employees. I will always be better for having worked here, and for having worked under people who push me to be the best version of myself.”

Standing by the work: “Part of what attracted me to MongoDB was the product market fit, and the more time I spend here, the stronger our position in the market becomes. It’s evident that MongoDB’s commitment to excellence and customer understanding applies not just to the sales process, but to the product department as well. We continue to develop features that make our customers’ lives easier and make their businesses more successful, like search and data tiering. With deep understanding of customer challenges, our product team delivers on meaningful enhancements to the platform at just the right time. It’s exciting to work with customers on a product that is intimately tied to crucial parts of their business and one that even dictates what’s possible for their own product and sales teams.”

Akeem McLennon

Lead Engineer, Kids

Literati is a literature technology company and subscription service for readers of all ages. 

Team driven: “I am always in awe of the people whom I work with on a day-to-day basis,” McLennon said. “Beyond the generally friendly nature of their personalities, they all bring with them a level of passion that has a visible impact on the work they bring to the company. This translates to both dedication to improving the product and being great people to work with. We are a company that is utilizing great technology to support an amazing product focused on innovation in the book industry. I am so proud to be a part of it and to work with such amazing and talented colleagues.”

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

Article originally posted on Data Science Central. Visit Data Science Central

Instead of focusing on “Automated Machine Learning” or AutoML, maybe we should focus on “Automated Data Management” or AutoDM?

You probably know that feeling. You start a blog with some ideas to share, but everything changes once you get started. That’s what happened with this blog.  I discussed the promise and potential of Automated Machine Learning (AutoML) in my blog “What Movies Can Teach Us About Prospering in an AI World“.  It seems quite impressive.

So, I decided to conduct a LinkedIn poll to garner insights from real-world practitioners, folks who can see through the hype and BS (and that’s not Bill Schmarzo…or maybe it should be) about the potential ramifications of AutoML.  It was those conversations that lead to my epiphany. But before I dive into my epiphany, let’s provide more on AutoML.

What is AutoML?

“Automated machine learning (AutoML) automates applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw data to the deployable machine learning model. The high degree of automation in AutoML allows non-experts to create ML models without being experts in machine learning. AutoML offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.[1]” See Figure 1.

Figure 1: Image sourced from: “A Review of Azure Automated Machine Learning (AutoML)”

Man, that is quite a promise. But here’s the AutoML gotcha: to make AutoML work, data experts need to perform significant data management work before getting into the algorithm selection and hyperparameter optimization benefits of AutoML.  This includes:

• Data Pre-processing which includes data cleansing (detecting and correcting corrupt or inaccurate records), data editing (detecting and handling errors in the data), and data reduction (elimination of redundant data elements).
• Data wrangling which transforms and maps data from one “raw” data format into a format that is usable by the AI/ML models.
• Feature Engineering which is the process of leveraging domain knowledge to identify and extract features (characteristics, properties, attributes) from raw data that are applicable to the problem being addressed.
• Feature Extraction involves reducing the number of features or variables required to describe a large set of data. This likely requires domain knowledge to identify those features most relevant to the problem being addressed.
• Feature Selection is the process of selecting a subset of relevant features (data variables) for use in model construction. Again, this likely requires domain knowledge to identify those features most relevant to the problem being addressed.

That’s a lot of work to do before even getting into the AutoML space.  But us old data dogs already knew that 80% of the analytics work was in data preparation.  It’s just that today’s AI/ML generation needs to hear that, and who better to deliver that message than one of the industry’s AI/ML spiritual leaders – Andrew Ng.

Andrew Ng: Become More Data-Focused and Less Algorithm-Focused

Here is a must watch video from by Andrew titled “Big Data to Good Data: Andrew Ng Urges ML Community to Be More Data-Centric And Less Model-Centric”.  There are lots of great insights in the video, but what struck me was Andrew’s own epiphany on the critical importance of spending less time tweaking the AI/ML models (algorithms) and investing more time on improving the data quality and completeness that feeds the AI/ML models. Andrew’s message is quite clear:  while tweaking the AI/ML algorithms will help, bigger improvements in overall AI/ML model performance and accuracy can be achieved by quality and completeness improvements in the data that feed the AI/ML algorithms (see Figure 2).

Figure 2: Transitioning from Algorithm-centric to Data-centric AI/ML Model Development

And note that those improvements in data quality and completeness that feeds the AI/ML models will benefit all AI/ML models that use that same data!  Sounds a lot like the Schmarzo Economic Digital Asset Valuation Theorem – the economic theorem on sharing, reusing, and refining of the organization’s data and analytic assets.

In the video, Andrew shared hard data with respect to improvement in results from tweaking the model (algorithm) versus improving data quality and completeness (see Figure 3).

Figure 3: Improving the Code versus Improving the Data

In the three use cases in Figure 4, there was literally no improvement in AI/ML model accuracy and effectiveness from tweaking the AI/ML models.  However, efforts applied against improving the data yielded quantifiable improvements, and in one case, very significant improvements!

LinkedIn Poll:  What Is True About AutoML?

Figure 4 shows the LinkedIn poll results where I asked participants to select the option they felt was most true about AutoML (sorry, only 4 options are available on LinkedIn).

Figure 4: LinkedIn AutoML Poll

If we factor the “All of the Above” choice with the top two choices, we get the following results:

• 62% of respondents feel AutoML will help automate data science model development
• 56% of respondents feel AutoML will enable business users to build their own ML models

Unfortunately, not having a “None of the Above” option was unfair because the results of the poll differ from poll comments. Here is my summary of those comments:

• AutoML will not be replacing data scientists anytime soon. However, AutoML can help jumpstart the Data Science process in ML model exploration, model selection, and hyperparameter tuning.
• AutoML will not suddenly turn business analysts into data scientists. That’s because ~80% of the ML model development effort is still focused on data preparation. To quote one person, “AutoML by untrained users would be like giving an elite athlete training plan and diet to average people and expecting elite results.”
• AutoML will be even more lacking as Data Scientist’s data preparation work evolves to semi-structured (log files) and unstructured data (text, images, audio, video, smell, waves).
• Realizing the AutoML promise will require a strong metadata strategy and plan.
• AutoML could help in AI/ML product management as the number of production ML models grows into the hundreds and thousands. But AutoML would need an automated set-up to monitor and correct for ML data drift while in production.
• Automating the ML process is just a small step. AutoML results need to be explainable to help in the evaluation of the analytic results using techniques such as SHAP or CDD.
• AutoML is a commodification of the loops and utilities that ML folks run through various ML algorithms, tune hyper-parameters, create features, and calculate metrics of all kinds.
• AutoML can be a great tool to get align teams around an organization’s ML aspirations. A field only flourishes when everyone from every discipline can use it to try different ideas.
• For AutoML to be successful, it is critical important to scope, validate, and plan the operationalization of the problem that one is trying to solve (e.g., Is the target variable here *really* what you want to model? Are all of the inputs available in a production environment? What decisions will this model support? How will you monitor the ongoing accuracy and usage of the model? How will you govern changes to the model, including commissioning and decommissioning it?). Hint, see Hypothesis Development Canvas?
• Finally, is AutoML a marketing ploy by cloud vendors to broaden their appeal to include enabling business users to build their own ML models?

I suggest that you check out the chat stream.  The comments were very enlightening.

AutoML or AutoDM Summary

My takeaway is that the concept of AutoML is good, but scope of the AutoML vision is missing 80% of the AI/ML model development and operationalization – providing high quality and complete data that feeds the AI/ML models. Figure 5 from “Big Data to Good Data: Andrew Ng Urges ML Community to Be More Data-Centric And Less Model-Centric” nicely summarizes the broader AutoML challenge with respect to data management.

Figure 5: Scope of What AutoML Needs to Address

Instead of focusing on “Automated Machine Learning” or AutoML, maybe we should focus on “Automated Data Management” or AutoDM?

Now that’s a thought…

[1] Wikipedia, AutoML https://en.wikipedia.org/wiki/Automated_machine_learning

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Los Angeles, United States, North America including Q1-2021 analysis The report named, Global NoSQL Database Market has been added to the archive of market research studies by JCMR. The industry experts and researchers have offered reliable and precise analysis of the NoSQL Database in view of numerous aspects such as growth factors, challenges, limitations, developments, trends, and growth opportunities. This NoSQL Database report will surely act as a handy instrument for the market participants to develop effective strategies with an aim to reinforce their market positions. This NoSQL Database report offers pin-point analysis of the changing dynamics and emerging trends in the Global NoSQL Database Market.

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What the NoSQL Database Report has to Offer?

• NoSQL Database Market Size Estimates: The report offers accurate and reliable estimation of the market size in terms of value and volume. Aspects such as production, distribution and supply chain, and revenue for the NoSQL Database are also highlighted in the report
• NoSQL Database Analysis on Market Trends: In this part, upcoming market trends and development have been scrutinized
• NoSQL Database Growth Opportunities: The report here provides clients with the detailed information on the lucrative opportunities in the NoSQL Database
• NoSQL Database Regional Analysis: In this section, the clients will find comprehensive analysis of the potential regions and countries in the global NoSQL Database
• NoSQL Database Analysis on the Key Market Segments: The report focuses on the segments: end user, application, and product type and the key factors fuelling their growth
• NoSQL Database Vendor Landscape: Competitive landscape provided in the report will help the companies to become better equipped to be able to make effective business decisions

Get Customized full NoSQL Database Report in your Inbox within 24 hours @ jcmarketresearch.com/report-details/1388120/enquiry

How can the NoSQL Database research study help your business?

(1) The information presented in the NoSQL Database report helps your decision makers to become prudent and make the best business choices.

(2) The report enables you to see the future of the NoSQL Database and accordingly take decisions that will be in the best interest of your business.

(3) It offers you a forward-looking perspective of the NoSQL Database drivers and how you can secure significant market gains in the near future.

(4) It provides SWOT analysis of the NoSQL Database along with useful graphics and detailed statistics providing quick information about the market’s overall progress throughout the forecast period.

(5) It also assesses the changing competitive dynamics of the NoSQL Database using pin-point evaluation.

Get up to 40% Special Discount on full research report @ jcmarketresearch.com/report-details/1388120/discount

The report answers several questions about the Global NoSQL Database Market includes:

What will be the market size of NoSQL Database market in 2029?
What will be the NoSQL Database growth rate in 2029?
Which key factors drive the market?
Who are the key market players for NoSQL Database?
Which strategies are used by top players in the market?
What are the key market trends in NoSQL Database?
Which trends and challenges will influence the growth of market?
Which barriers do the NoSQL Database markets face?
What are the NoSQL Database market opportunities for vendors and what are the threats faced by them?
What are the most important outcomes of the five forces analysis of the NoSQL Database market?

Buy Instant Full Copy of Global NoSQL Database Report, 2021-2029 @ jcmarketresearch.com/checkout/1388120

Find more research reports on NoSQL Database Industry. By JC Market Research.

• NoSQL Databases Software Industry Analysis, Market Size, Share, Trends, Growth and Forecast 2021 – 2029
• NoSQL Database Industry Analysis, Market Size, Share, Trends, Growth and Forecast 2021 – 2029
• NoSQL Database Industry Analysis, Market Size, Share, Trends, Growth and Forecast 2021 – 2029

About Author:

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Article originally posted on InfoQ. Visit InfoQ

Practically every development team would benefit from building and running applications that use the advantages of the cloud computing delivery model. This August 17th at InfoQ Live software leaders dive into topics such as service mesh, CI/CD, cloud native best practices and pitfalls, and more. Join them and book your spot now for just $19.95.

InfoQ Live Session Spotlights

Deploying Service Mesh in Production by Christian Posta, Global Field CTO @soloio_inc 

Deciding whether or not to use a service mesh and understanding the value/complexity tradeoff is step one when exploring service-mesh technology. 

Christian Posta has been helping organizations adopt this technology as a practitioner and architect for the last three (or more) years. In this session he will share practical guidance for how to adopt a service mesh in your organization, including separating out control plane and data plane, plugging in with observability tools, leveraging gateways appropriately, rolling out mTLS safely, and overall preparing for troubleshooting and debugging. This talk will include a lot of live demos to illustrate the concepts.

9 Ways To Fail At Cloud Native by Senior Technical Staff Member & Innovation Leader @IBM Holly Cummins.

Cloud native is the perfect recipe for innovation, adaptability, and engineering excellence. Right? Well, when it goes right. When it goes wrong, sometimes it’s monster spaghetti, sometimes it’s a quality headache, and – worst of all – sometimes it’s exactly as clunky and slow-to-change as what it’s replacing. As a consultant, Holly gets to see really good practices and also the anti-patterns; in this talk, she’ll share stories of what happens when things go wrong.

InfoQ Live is focused on real-world advice you can implement. By joining us you can expect:

• Best practices and lessons learned on how world-class software professionals solved common challenges.
• Live interactive Q&As with the speakers to get answers to your questions.
• Practical ways to guide your problem-solving approach.

Book your spot at InfoQ Live on August 17th. If you register you’ll get exclusive access to all talks on-demand. 

*We are donating 100% of net ticket revenue for this event to organizations working towards diversity, equity, and inclusion in the technology industry.

Posted on nosqlgooglealerts. Visit nosqlgooglealerts

Version 4.0 of Apache Cassandra, the open source NoSQL distributed database, has finally arrived with improvements including much faster performance and a promise that this release is the most stable yet.

Apache Cassandra handles massive amounts of data across load-intensive applications with high availability and no single point of failure.

High-profile users include Apple, which has over 160,000 instances storing over 100 petabytes of data across 1,000+ clusters, Huawei and Netflix, where Cassandra handles over 1 trillion requests per day. Cassandra was developed by Facebook in 2008, and became an Apache Top-Level Project in 2010.

The improvements to the new version start with increased speed and scalability. Cassandra 4 streams data up to five times faster during scaling operations, and is up to 25% faster throughput on reads and writes.

Consistency is better; the new release keeps data replicas in sync to optimize incremental repair. The security and manageability is also improved with better audit logging that tracks users’ access and activity, and new capture and replay to ensure regulatory and security compliance. 

Latency has been improved through work on the garbage collector to reduce pause times as heap sizes increase, and compression is also more efficient, which improves read performance.

The last major release of Cassandra was in 2015 when version 3.0 was released. There have been 3.x releases since then, but the project team say the long gap till 4.0 is because they decided to become uncompromising on one important feature: quality. The intention is to avoid the situation where x.0 releases are avoided because of quality issues.

The Cassandra team says that the scale that Cassandra clusters can reach means that there is an enormous surface area for potential bugs or data corruption, so they put in place tools including property-based / fuzz testing, replay testing, performance testing and fault injection to ensure this and future releases maintain a high level of quality and correctness. The testing resulted in over 1,000 bugs being identified and fixed, many of which were only found in the largest scale production workloads.

The aim was to have Cassandra 4.0 at a state at release where major users would run it in production. Cassandra 4.0 is already running in production today at Apple, DataStax, Instaclustr, Netflix, Orange, Pythian, Sky UK, and Yelp.    

More Information

Cassandra Website

Related Articles

DataGrip Adds Cassandra Support

Instaclustr Releases Cassandra Tools

Cassandra 2.0 Available

Cassandra 1.0 with Increased Performance

Cassandra with CQL

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Article originally posted on Data Science Central. Visit Data Science Central

The concept of remote work was alien to almost every professional until March 2020 when the World Health Organization declared COVID-19 a deadly pandemic.

After the severity of the situation increased, every organization, every professional had to adapt to remote working mandatorily as the governments across the world announced complete lockdowns and major restrictions on the movement.

With no prior experience and understanding of how remote work is carried out at scale, organizations, business leaders, HR leaders, senior and mid-level managers, as well as executive-level staff struggled to find the right spot between communication, executive, and innovation.

The first half of 2020 was a learning curve for everyone while they accepted and understood the work from home reality, and adapted accordingly. But 2021 is definitely the year where new norms of working are getting defined and remote working is going to play a really big part. According to a survey done by Tecla, 85% of managers believe that teams with remote work will become the future of work.

“The future we envision for work allows for infinite virtual workspaces that will unlock social and economic opportunities for people regardless of barriers like physical location. It will take time to get there, and we continue to build toward this.” – Andrew Bosworth, VP Facebook Reality Labs

Since remote work is the future, every manager should know the nuances, best practices, and strategies of managing a remote team which is what we are going to discuss in this article.

[Tried & Tested] Tips & Strategies to Manage a Remote Team in 2021

All the strategies and tips listed below to manage a remote team are based on the biggest struggles of working remotely.

[Source: Buffer]

Communicate All the Kinks Out

It’s easier to communicate but it’s the hardest to communicate clearly and effectively. The better the communication, the easier it will be for you to set expectations, communicate deadlines and project specifics.

Use deliberate and structured communication whenever communicating with your team members. If possible, have weekly work meetings on Mondays to set goals for the week and then a sort of debriefing and fun meetings on Fridays for catching up, understanding progress on projects, weekend plans, etc. This way, you hit the sweet spot between formal, productive, and informal communication.

Also, the key to successfully conducting these meetings is video calls, so always make sure your team is switching their videos on and proactively participating in conversations. To be more effective, invest time in your team members – have a habit of conducting 1-1 meetings with them on a regular basis to understand how they are doing.

“Technology now allows people to connect anytime, anywhere, to anyone in the world, from almost any device. This is dramatically changing the way people work, facilitating 24/7 collaboration with colleagues who are dispersed across time zones, countries, and continents. ” — Michael Dell, Dell

Establish Complete Feedback Loops

Having feedback loops is quite important for remote teams since everyone is working remotely and in different time zones.

Establishing a feedback culture will help you provide support to your team members at an individual level, identify pain areas in operations, stay ahead of any potential conflict, and build meaningful relationships. All this will help improve your team’s overall performance.

Tips on How to Build Feedback Culture

• Make it a part of your process from day 1
• Create a safe environment for your team to express their feedback and concerns openly
• Train your team to give receive feedback – it is essentially a skill
• Use different feedback channels like 1-on-1 meetings, 360 feedback, anonymous feedback, etc.

Boundaries are Productive

After working from home for over a year, all of us have realized that the lines between work life and personal life can easily get blurred when you are working from home. So, it’s important to set some healthy boundaries for all your remote team members to avoid extra, unnecessary stress, and burnout.

For example, recently, Bumble – a dating app company, announced a week-long holiday for all their employees to avoid burnout.

A Few Ways to Go About Setting Boundaries

• Limit availability
• Ask them to avoid connecting their professional accounts on their personal devices
• Encourage wellness and self-care activities like mindful meditation breaks
• Share about personal interests and hobbies or any other non-work talks to keep it light
• Most importantly, don’t schedule too many meetings

Invest in Right Tools & Technologies

Since all of your team members are scattered across different cities, countries, and even continents, it becomes imperative to invest in the right tools and technologies that enable effective timely collaboration.

Things to Keep in Mind While Choosing Tools for Your Team

• Consider all the use cases and then hunt for the right product.
  

Choose future-proof tools that enable digital transformation for your organization. For example, if you were planning to invest in SaaS tools that enhance customer experience, then instead of going for software that enables better communication between your team and customers, invest in software that adds to their experience directly like a product tour software for customers’ onboarding. 

Here are some product tour examples and a guide on how to make the most out of such software.

• Review your process and needs and then choose accordingly
• Do your research, thoroughly
• Get the tools customized, if needed
• Pick the tools that allow you to create an integrated ecosystem
• Train your team

“The whole conversation is about how remote work is different, instead of being about the amazing tools we have at our disposal that remote teams and non-remote teams are able to use at any time. We have this opportunity to have a lot more freedom in our environment compared to when we had to be in an office, or even in school, 40 hours per week.” — Hiten Shah, FYI

Mandatory Monthly or Quarterly Holidays

If your team members haven’t opted for any holidays in the last few months, then make sure they take holidays, mandatorily. Just because we all are working from home and can’t travel, for the time being, doesn’t mean we don’t need holidays.

So make sure to keep an eye out for your team by encouraging them to time off even if they think they don’t need it.

Lastly, Acknowledge & Celebrate Milestones and Hard Work

Celebrating achievements and milestones is quite necessary to keep everyone motivated. Before remote working became the norm, it used to be easy to gather everyone and celebrate individual achievements, company-wide milestones. But when everyone is working from their own spaces, celebrating achievements gets forgotten easily. 

So, make sure to make it a habit to celebrate and acknowledge your team members whenever they are due.

“Now that companies have built the framework – and experienced the cost and time savings associated with it – there’s no real reason to turn back.”  –  Mark Lobosco