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MongoDB Atlas Adds Serverless Option

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Posted on mongodb google news. Visit mongodb google news

Installing and maintaining database software, either on your own server or on a virtual cloud server, is a pain. You need to keep software up to date, ensure proper security, and fix any of the myriad problems that could arise at any time. If your database goes down, your application goes down with it.

Atlas, a database-as-a-service offering from MongoDB (NASDAQ:MDB), largely solves these problems. With Atlas, MongoDB handles many of the details, including security, backups, and performance optimization. There are no servers to manage for an Atlas customer, just a database running on one of the big cloud platforms.

The MongoDB Atlas logo.

Image source: MongoDB.

Going fully serverless

Until recently, Atlas came in two flavors. The low-end shared version of Atlas runs on hardware that’s shared with other customers. This means that the usage patterns of other applications can affect your own application. For a production app with thousands of users, this low-cost version of Atlas isn’t ideal.

Dedicated Atlas provides each customer with their own hardware to run their database, along with other advanced features not available in the shared version. This solves the main issue with the shared version, but there are still two sticking points. First, the lowest-cost dedicated plan starts a $57 per month, many times more than it would cost to simply install and run a database on a dedicated virtual server. Second, there are a lot of decisions that need to be made regarding compute power and capacity.

It may be difficult to predict how powerful a database you’ll need for an application, and the requirements may change over time. The amount of compute power, memory, and storage all need to be chosen, and those decisions affect the cost of your dedicated Atlas cluster. You remove the need to manage servers with Atlas, but you still need to manage the database itself. If your app has periodic spikes in usage, you need a database powerful enough to handle those spikes. During off-times, your database will sit largely idle.

Earlier this month, MongoDB introduced a third flavor of Atlas that aims to alleviate the downsides of the dedicated Atlas plans. Serverless Atlas is still in preview, but it provides developers a way to pay for actual usage and not have to worry about deciding on specs.

The only decision a developer needs to make with serverless Atlas is which cloud provider and cloud region to use. Atlas takes it from there, dynamically scaling up and down resources as needed. Customers pay a fixed rate for each document read and document write, as well as rates for storage and data transfer.

This is similar to how other cloud-based database services work. Firestore from Alphabet‘s Google Cloud, for example, is fully serverless and charges for reads, writes, storage, and bandwidth in the same way. With serverless Atlas, MongoDB now competes more directly with other usage-based cloud database services.

Lowering the cost of entry

Atlas has become MongoDB’s main growth engine, with sales from the database-as-a-service soaring 73% in the company’s first quarter. Atlas now accounts for more than half of MongoDB’s total revenue. There’s no question that Atlas is a popular option for developers.

However, the high cost of a dedicated Atlas cluster may be keeping some potential customers away, especially those who only need a powerful database some of the time to deal with spikes in usage. Serverless Atlas provides an option for those customers.

MongoDB will be working with partners to help make serverless Atlas a seamless option for developers who have already embraced a serverless application architecture. The company is teaming with Vercel and Netlify, two serverless app development platforms, to integrate serverless Atlas. These partnerships will open up Atlas to new customers who may not have considered it in the past, or who assumed it wasn’t a good fit.

Atlas, and services built around it, are the key to MongoDB’s long-term growth story. With serverless Atlas, the company has made it easier and cheaper to use Atlas for a production application.

This article represents the opinion of the writer, who may disagree with the “official” recommendation position of a Motley Fool premium advisory service. We’re motley! Questioning an investing thesis — even one of our own — helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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

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What is data preparation?

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

Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It has also gotten easier with the self-service data preparation tool that enables users to cleanse and qualify on their own.

Data preparation:

In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.

Importance of data preparation

Fix errors quickly – Data Preparation process helps to catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.

Top-quality data – Data Cleansing and reformatting datasets ensure that all data used in the analysis will be high quality.

Better business decision – Higher quality data can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.

Superior scalability – Cloud data preparation can grow at the pace of the business.

Future proof – Cloud data preparation automatically upgrades so that new capabilities or bug fixes can be triggered as soon as they are released. This allows organizations to stay ahead of the future betterment without risking delays or additional costs.

Accelerated data usage and collaboration – Doing data preparation in the cloud is always on, does not require any technical installation, and lets teams collaborate on the work for faster results.

Now, The Self-service Data Preparation process has become faster and more accessible to a wider variety of users.

To learn more about data preparation, Schedule a demo.

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Smart Waste Management: AI Leads the Way

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

The amount of waste generated in the world amounted to billions (metric tons) in quantity at the end of 2020. With the growing pressure of the human population, it is set to escalate further. In the developed nation, waste management is a profit-making market vertical. Innovative methodologies and techniques are helping make waste management seem an opportunity to bank upon. The sector is steadily progressing at a CAGR of 5.5% annually. Meanwhile, the global waste management market is set to reach USD 2.3 trillion in the upcoming five to six years.

Waste management scenario in the world

In terms of waste management, the sector encompasses – waste dumping, recycling, and minimization. The main categories are segmented as municipal waste, industrial waste, e-waste, plastic waste, biomedical waste, and others, across the world. As per the World Bank report, the regions producing waste across the world are East Asia and the Pacific region. Often, changes in incremental income in low-income levels have resulted in the production of more solid waste and other types of waste; meanwhile, waste production has also been associated with factors like high income and population to add to the numbers.

Here is a snapshot of trends in waste production, especially solid waste category in the past few years and what the leading trends look like, region-wise.

Image credit: datatopics.worldbank.org

Several entities in the waste management domain have emerged in the past few years. By making use of comprehensive processes to deal with escalating waste piles, many firms belonging to North America and European regions especially, are developing techniques to process the waste and are diligently working on waste minimization procedures. In this goal, technology has become integral to handling the burden posed by categories of waste. Some of the leading nations to work towards developing innovative ways for waste handling and minimization are – Germany, Switzerland, Austria, Sweden, Singapore, among others.

Solving waste management concerns

The primary methods used for waste management include landfill, incineration, composting, and recycling. Out of these, incineration and composting help in reducing the volume of waste to a considerable extent. Other methods to tackle waste include disposal at compost areas, volume reductions plants, borrow pit reclamation areas, and processing locations. While landfilling or decomposition contributes to GHG or greenhouse emissions that cause maximum negative effects than harmful carbon dioxide or CO2. As an active contributor in producing GHG, waste decomposition is far more harmful than carbon dioxide for the environment. Starting from open waste dumps to waste decomposition, the main reason why waste management is mandatory is the deterioration of the environment and human surroundings. Today, it is being helmed as a leading cause of climate change and creating various health risks. Additionally, waste in various forms is posing a great health risk to health workers who are involved in the collection and dumping of the waste on a day-to-day basis.

Although, things are changing fast now with the adoption of practices that are carried out through technological intervention. Technology-led initiatives in waste minimization are influencing the way waste is collected, transported, and sent for recycling. With the onset of internet-of-things or IoT, the possibilities and methods to recycle, upcycle and decomposition processes have become more streamlined and attainable.

The AI-enabled smart waste management

In terms of waste management, traditional waste management techniques have proven to be complex, labor-intensive, and often pose a risk to the life of sanitation workers and staff. On the other hand, a connected ecosystem inspired by IoT has paved the way for the application of AI and Machine Learning models for channeling multiple elements for better Urban Planning and smart cities or cities of the future.

Several developed nations have successfully implemented the AI-enabled waste management infrastructure to reduce waste and process recyclable material. Smart Bins equipped with scanners can scan each and every object discarded by an individual and save the data for transfer remotely through a sensor. The bins can segregate different types of waste like metal, paper, glass, plastic, organic, etc while it gets detected as a frozen inference graph through a camera attached inside with the processing unit. AI programs powered by Machine learning and accurate computer vision training data help in classifying different types of waste images and help in the detection of their categories. Post this, an embedded ultrasonic sensor device also checks the filling level and notifies the owner of the usage. Once the trash bins are filled, the sensors notify centralized waste management systems, which then turn up to collect the waste.

Further, once the collection of trash is done, the Smart Bins are taken to waste processing facilities. Herein, the waste processing facilities working with Artificial Intelligence-based programs, identify the types of waste material and start the segregation on the basis of inference graph data. The segregated waste is sent for the next level of processing for various other methods of waste recycling. Items like metal, cardboard, plastic, wood, and electronic equipment are recycled and made contamination-free for the production of goods.

Last word

The never-ending cycle of waste production and disposal has crippled the existing infrastructure and over-pressurized manpower for a long time. AI-enabled smart waste management systems are a viable answer to the health risks posed, time and energy costs involved in the collection and disposal of waste. With the burden of the growing population and exhaustion of landfill sites, smart waste management has become an imperative and a must-have option to live on a waste-free planet. Not merely this, it will help in tackling waste disposal issues but will also contribute to the creation of a healthier environment. Rather than following decades-old techniques, Smart Waste Management-focused AI applications have opened up new facets to tackle the persistent problem of waste management, especially in countries with swelling populations.

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Global In-Memory Computing Market Size, Key Players Analysis and Forecast To 2027 : Oracle …

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Posted on mongodb google news. Visit mongodb google news

In-Memory Computing

The report on the Global In-Memory Computing Market Size, Share, Growth, Trends Analysis, Segmentation, Regional Outlook and Forecasts, 2021 – 2027 market documented by Zion Market Research (ZMR) means to offer a coordinated and orderly methodology for the major aspects that have influenced the market in the past and the forthcoming market prospects on which the organizations can depend upon before investing. It furnishes with a reasonable examination of the market for better decision-making and assessment to put resources into it. The report analyses the elements and a complete detailed outlook of the main players that are probably going to add to the demand in the global In-Memory Computing market in the upcoming years.

The top Major Competitive Players are : Oracle, Software AG, Red Hat, MongoDB, Salesforce, Kognitio, Intel, Enea, Fujitsu, and SAP

FREE : Request Sample is Available @ https://www.zionmarketresearch.com/sample/in-memory-computing-market

The market report additionally gives a to-the-point evaluation of the techniques and plans of action that are being executed by the players and companies to contribute to the global In-Memory Computing market growth. Some of the most conspicuous measures taken by the organizations are partnerships, mergers & acquisitions, and collaborations to extend their overall reach. The players are likewise presenting newer product varieties in the market to improve the product portfolio by embracing the new innovation and carrying out it in their business.

Global In-Memory Computing Market: Regional Analysis

  • North America ( United States)
  • Europe ( Germany, France, UK)
  • Asia-Pacific ( China, Japan, India)
  • Latin America ( Brazil)
  • The Middle East & Africa

The report on the global In-Memory Computing market utilizes diverse methods to examine the market data and present it in an organized manner to the readers. It provides the market research on the various segmentation based on the aspects like region, end-user, application, types, and other important categories. It further gives a detailed report on the leading sub-segment among each of them.

Download Free PDF Report Brochure @ https://www.zionmarketresearch.com/requestbrochure/in-memory-computing-market

Moving to the drivers and restraints, one will be given all factors that are indirectly or directly helping the development of the global In-Memory Computing market. To get to know the market’s development measurements, it is important to evaluate the drivers of the market. Furthermore, the report likewise analyzes the current patterns alongside new and plausible growth openings for the global market. Additionally, the report incorporates the components that can restrict the market growth during the forecast period. Understanding these elements is also mandatory as they help in grasping the market’s shortcomings.

Primary and secondary methodologies are being utilized by the research analysts to gather the information. Along these lines, this global In-Memory Computing market report is planned at guiding the readers to a superior, clearer viewpoint and information about the global market.

COVID-19 impact: Since the pandemic has adversely affected almost every market in the world, it has become even more important to analyze the market situation before investing. Thus, the report comprises a separate section of all the data influencing the market growth. The analysts also suggest the measures that are likely to uplift the market after the downfall, bettering the current situation.

The study objectives of this report are:

  • To study and analyze the global Keyword size (value and volume) by the company, key regions/countries, products and application, history data from 2020 to 2027, and forecast to 2027.
  • To understand the structure of Keyword by identifying its various sub-segments.
  • To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
  • Focuses on the key global Keyword manufacturers, to define, describe and analyze the sales volume, value, market share, market competition landscape, SWOT analysis, and development plans in the next few years.
  • To analyze the Keyword with respect to individual growth trends, future prospects, and their contribution to the total market.
  • To project the value and volume of Keyword submarkets, with respect to key regions (along with their respective key countries).
  • To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
  • To strategically profile the key players and comprehensively analyze their growth strategies

Inquire more about this report @ https://www.zionmarketresearch.com/inquiry/in-memory-computing-market

Frequently Asked Questions

  • What are the key factors driving In-Memory Computing Market expansion?
  • What will be the value of In-Memory Computing Market during 2021- 2027?
  • Which region will make notable contributions towards global In-Memory Computing Market revenue?
  • What are the key players leveraging In-Memory Computing Market growth?

Also, Research Report Examines:

  • Competitive companies and manufacturers in global market
  • By Product Type, Applications & Growth Factors
  • Industry Status and Outlook for Major Applications / End Users / Usage Area

Thanks for reading this article ; you can also get individual chapter-wise sections or region-wise report versions like North America, Europe, or Asia.

Also Read, Global Cell Based Assay Market Report

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

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Industry 4.0 in CNC Machine Monitoring

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

The demand for Computer Numerical Control (CNC) equipment is gradually increasing and performing to expect a huge growth over the coming years. For this an annual growth rate of more than six percent. CNC machining plays a major role in present manufacturing and helps us create a diverse range of products in several industries, from agriculture, automotive, and aerospace to Semiconductor and circuit boards.

Nowadays, machining has developed rapidly in periods of processing complexity, precision, machine scale, and automation level. In the development of processing quality and efficiency, CNC machine tools play a vital role.

Faststream Technologies has implemented the IoT-enabled CNC machine monitoring solutions, which creates machine-to-machine interaction resulting in automated operations and less manual intervention.

Embedded the IoT sensors on CNC machines that can measure various parameters and send them to a platform from where the state and operation of the machines can be fully supervised. Furthermore, CNC machines can scrutinize the data collected from sensors to perpetually replace tools, change the degree of freedom, or perform any other action.

ADVANTAGES:

An Enterprise can leverage the following advantages by coalescence of Industry 4.0 and CNC.

Predictive Maintenance:

CNC Machine operators and handlers embrace the Industrial IoT which allows them to appropriately interconnect with their CNC machines in many ways through smartphones or tablets. Therefore the operators can monitor the condition of machines at all times remotely using Faststream’s IoT-based CNC machine monitoring.

This remote and real-time monitoring aids the machine operating person to program a CNC for a checkup or restore.

On the other hand, these can also arrange their CNC machines to send alerts or notifications to operators whenever machines deem themselves due for tuning or maintenance. In another term, the machine will raise red flags about complications such as a rise in temperature, increased vibrations, or tool damage.

Reducing Downtime and Efficient Machine Monitoring :

Digital Transformation in CNC Machine solutions has broad competence and is not restricted to distant control and programmed maintenance for CNC machines.

Reduce machine downtime and escalate overall equipment effectiveness by using our IoT system and grasping its real-time alert features. The Alerts received from machines can be used to do predictive measures and unexpected breakdown of tools or any other element of a CNC machine.

Faststream Technologies similar solutions to its clients by arranging the IoT energy management solution for their CNC machines. Pre-executing these solutions, the client was facing difficulties with the future breakdown of their machines. Faststream’s IoT solution guided them to retain a clear insight into the running hours of their CNC machines, which in turn gave them exact thoughts of how they were maintaining their production run-time.

Machine downtime reducing solutions can be utilized for a chain of CNC machines to not only ameliorate their processing but also to boost the machine synchronization process in industrial inception and realize the operational eminence.

Less manual effort and Worker Safety:

For the bigger enactment, the technology of Industrial IoT can also be implemented to bring down manual efforts, or in other terms, mitigate the possibility of workers’ injury in the factory operation process.

From this action, machine-to-machine synchronization and interrelation come into the picture. The synergy between machines will result in more interpretation between various electromechanical devices, which will lead to automated operations in a Manufacturing unit.

Many companies are already working towards the development of smart robots and machines that can.

Several Companies that perform on smart robots and machine development can work on pre-programmed tasks and retaliation to the existing needs of CNC machines for bringing down the extra strain of quality operation from the manual workforce. All these robots can perform confined and elegant work like opening & close the slab of a CNC machine or reform the tool whenever sharpness is required.

Apart from the lowering injuries in the workshop, our Industry 4.0 in CNC Machine also helps in lowering material wastage and betterment the efficiency of CNC machines, which will help in the rise in production of exact elements in a shorter time frame.

 

CONCLUSION:

CNC machines are electromechanical devices that can operate tools on a different range of axes with more accuracy to generate a small part as per command put through a computer program. These can run faster than any other non-automated machine as well as can generate further objects with high accuracy from any type of design.

Using the technology of the Industrial Internet of Things(IIOT), the competence of a company can be boosted even further, though CNC machines are themselves proficient in uplifting a machine to a new peak.

Faststream Technologies is a cutting-edge IoT solution provider that assists factories and workshops to integrate their CNC machines with Industry 4.0 solutions.

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S2, A next generation data science toolbox

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

 

We have created a language that is faster than python in every way, works with the entire Java ecosystem (such as the Spring framework, Eclipse and many more) and can be deployed into embedded devices seamlessly, allowing you to collect and process data from pretty much any device you want even without internet.

Our language comes built-in with mathematical libraries necessary for any data scientist, from basic math like Linear Algebra and Statistics to Digital Signal Processing and Time Series Analysis.

 

These algorithms have been developed by a team of Computer Science and Mathematics PhD’s from scratch over the course of a decade, and they are faster than Apache and R. Using our Linear Algebra library as a benchmark, we are 180 times faster than Apache and 14 times faster than R. (suanshu-3.3.0 is the old version of our language, NM Dev)

 

Our code can be prototyped and scaled for mass production in a single step, without the need for translation to different languages. With this feature, the time taken for you to actualise your ideas is significantly reduced and the need to go through the frustration of doing menial translation work is removed.

We can do this because our algorithms are written in Java and Kotlin, both of which are compatible with any environment that runs on a Java Virtual Machine unlike R or MATLAB which only work within their respective programming environments. This is our user interface, running on Jupyter notebook.

 

 

Overall, our language is faster than any specialised math software/scripting language and can be integrated seamlessly into most of the existing hardware and software available.

 

Our platform, S2, also comes with a GUI that allows easy visualisation of data (both 2D and 3D plotting) for teaching as well as analysing data.

 

If you are interested, check out our website here, we provide free trials!

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4 key areas SaaS startups must address to scale infrastructure for the enterprise – TechCrunch

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Startups and SMBs are normally the primary to undertake many SaaS merchandise. However as these clients develop in dimension and complexity — and as you rope in bigger organizations — scaling your infrastructure for the enterprise turns into important for fulfillment.

Beneath are 4 recommendations on methods to advance your organization’s infrastructure to assist and develop along with your largest clients.

Deal with your clients’ safety and reliability wants

If you happen to’re constructing SaaS, odds are you’re holding crucial buyer knowledge. No matter what you construct, that makes you a risk vector for assaults in your clients. Whereas safety is essential for all clients, the stakes actually get greater the bigger they develop.

Given the stakes, it’s paramount to construct infrastructure, merchandise and processes that handle your clients’ rising safety and reliability wants. That features the moral and ethical obligation you need to be certain that your methods and practices meet and exceed any declare you make about safety and reliability to your clients.

Listed below are safety and reliability necessities giant clients usually ask for:

Formal SLAs round uptime: If you happen to’re constructing SaaS, clients count on it to be out there on a regular basis. Giant clients utilizing your software program for mission-critical purposes will count on to see formal SLAs in contracts committing to 99.9% uptime or greater. As you construct infrastructure and product layers, you want to be assured in your uptime and have the ability to measure uptime on a per buyer foundation so you understand when you’re assembly your contractual obligations.

Whereas it’s onerous to prioritize asks out of your largest clients, you’ll discover that their collective suggestions will pull your product roadmap in a selected course.

Actual-time standing of your platform: Most bigger clients will count on to see your platform’s historic uptime and have real-time visibility into occasions and incidents as they occur. As you mature and specialize, creating this visibility for patrons additionally drives extra collaboration between your buyer operations and infrastructure groups. This collaboration is efficacious to put money into, because it offers insights into how clients are experiencing a selected degradation in your service and permits so that you can talk again what you discovered to this point and what your ETA is.

Backups: As your clients develop, be ready for expectations round backups — not simply when it comes to how lengthy it takes to recuperate the entire software, but additionally round backup periodicity, location of your backups and knowledge retention (e.g., are you holding on to the info too lengthy?). If you happen to’re constructing your backup technique, occupied with future flexibility round backup administration will enable you to keep forward of those asks.

Tech specialist. Social media guru. Evil problem solver. Total writer. Web enthusiast. Internet nerd. Passionate gamer. Twitter buff.

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How the world caught up with Apache Cassandra

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By Jeff Carpenter, DataStax

The O’Reilly book, Cassandra: The Definitive Guide, features a quote from Ray Kurzweil, the noted inventor and futurist: 

“An invention has to make sense in the world in which it is finished, not the world in which it is started.” 

This quote has a prophetic ring to it, especially considering my co-author Eben Hewitt included it in the 2010 first edition of this book we wrote, back when Apache Cassandra, the open-source, distributed, and highly scalable NoSQL database, was just on its 0.7 release. 

In those days, other NoSQL databases were appearing on the scene as part of platforms with worldwide scale from vendors like  Amazon, YouTube, and Facebook. With many competing database projects and a slowly emerging response from relational database vendors, the future of this emerging landscape wasn’t yet clear, and Hewitt qualified his assessment with this summary: “In a world now working at web scale and looking to the future, Apache Cassandra might be one part of the answer.” (emphasis added)

While many of those databases from the NoSQL revolution and the NewSQL counter-revolution have now faded into history, Cassandra has stood the test of time, maturing into a rock-solid database that arguably still scales with performance and reliability better than any other. 

Twelve-plus years after its invention, Cassandra is now used by approximately 90 percent of the Fortune 100, and it’s appeal is broadening quickly, driven by a rush to harness today’s “data deluge” with apps that are globally distributed and always-on. Add to this recent advances in the Cassandra ecosystem such as Stargate, K8ssandra, and cloud services like Astra DB, and the cost and complexity barriers to using Cassandra are fading into the past. So while it’s fair to say that while Cassandra might have been ahead of its time in 2007, it’s primed and ready for the data demands of the 2020s and beyond.

Cassandra made a lot of sense to its inventors at Facebook when they developed it in 2007 to store and access reams of data for Messenger, which was growing insanely fast. From the start, Cassandra scaled quickly, and accessed huge amounts of data within strict SLAs—in a way that relational databases and SQL, which had long been the standard ways to access and manipulate data, couldn’t. As it became clear that this technology was suitable for other use cases, Facebook handed Cassandra to the Apache Software Foundation, where it became an open source project (it was voted into a top-level project in 2010).

The reliability and fail-over capabilities offered by Cassandra quickly won over some rising web stars, who loved its scalability and reliability. Netflix launched its streaming service in 2007, using an Oracle database in a single data center. As the company’s streaming service users, the devices they binge-watched with, and data expanded rapidly, the limitations on scalability and the potential for failures became a serious threat to Netflix’s success. At the time, Netflix’s then-cloud architect Adrian Cockroft said he viewed the single data center that housed Netflix’s backend as a single point of failure. Cassandra, with its distributed architecture, was a natural choice, and by 2013, most of Netflix’s data was housed there, and Netflix still uses Cassandra today.

Cassandra survived its adolescent years by retaining its position as the database that scales more reliably than anything else, with a continual pursuit of operational simplicity at scale. It demonstrated its value even further by integrating with a broader data infrastructure stack of open source components, including the analytics engine Apache Spark, stream-processing platform Apache Kafka, and others.

Cassandra hit a major milestone this month, with the release of 4.0. The members of the Cassandra community pledged to do something that’s unusual for a dot-zero release: make 4.0 so stable that major users would run it in production from the get-go. But the real headline is the overall growth of the Cassandra ecosystem, measured by changes both within the project and related projects, and improvements in how Cassandra plays within anyour infrastructure. 

A host of complementary open-source technologies have sprung up around Cassandra to make it easier for developers to build apps with it. Stargate, for example, is an open source data gateway that provides a pluggable API layer that greatly simplifies developer interaction with any Cassandra database. REST, GraphQL, Document, and gRPC APIs make it easy to just start coding with Cassandra without having to learn the complexities of CQL and Cassandra data modeling.

K8ssandra is another open source project that demonstrates this approachability, making it possible to deploy Cassandra on any Kubernetes engine, from the public cloud providers to VMWare and OpenStack. K8ssandra extends the Kubernetes promise of application portability to the data tier, providing yet another weapon against vendor-lock in.

There’s a question that Hewitt poses in Cassandra: The Definitive Guide:  “What kind of things would I do with data if it wasn’t a problem?”

Netflix asked this question—and ran with the answer—almost a decade ago. The $25-billion company is a paragon of the kind of success that can be built with the right tools and the right strategy at the right time. But today, for a broad spectrum of companies that want to achieve business success, data also can’t be a “problem.” 

Think of the modern applications and workloads that should never go down, like online banking services, or those that operate at huge, distributed scale, such as airline booking systems or popular retail apps. Cassandra’s seamless and consistent ability to scale to hundreds of terabytes, along with its exceptional performance under heavy loads, has made it a key part of the data infrastructures of companies that operate these kinds of applications.

Across industries, companies have staked their business on the reliability and scalability of Cassandra. Best Buy, the world’s largest multichannel consumer electronics retailer, refers to Cassandra as “flawless” in how it handles massive spikes in holiday purchasing traffic. Bloomberg News has relied on Cassandra since 2016 because it’s easy to use, easy to scale, and always available; the financial news service serves 20 billion requests per day on nearly a petabyte of data (that’s the rough equivalent of over 4,000 digital pictures a day—for every day of an average person’s life). 

But Cassandra isn’t just for big, established sector leaders like Best Buy or Bloomberg. Ankeri, an Icelandic startup that operates a platform to help cargo shipping operators manage real-time vessel data, chose Cassandra—delivered through DataStax’s Astra DB—in part because of its ability to scale as the company gathers an increasing amount of data from a growing number of ships. It wanted a data platform that wouldn’t make data a problem, and wouldn’t get in the way of its success.

A handful of organizations have built services around Cassandra, in an effort to make it more accessible, and to solve some of the inherent challenges that come with operating a robust database.

One particularly hard nut to crack when it comes to managing databases has been provisioning. With cloud computing services (think AWS Lambda), scaling, capacity planning, and cost management are all automated, resulting in software that’s easy to maintain, and cost effective—”serverless,” in other words. But because modern databases store data by partitioning it across nodes of a database cluster, they’ve proved challenging to make serverless. Doing so requires rebalancing data across nodes when more are added, in order to balance storage and computing capabilities. 

Because of this, enterprises have been required to guess what their peak usage will be—and pay for that level, even if they aren’t using that capacity. That’s why it was a big deal when DataStax announced earlier this year that its Astra DB cloud database built on Cassandra is  available as a serverless, pay-as-you-go service. According to recent research by analyst firm GigaOm, the serverless Astra DB can deliver significant cost savings. And developers will only pay for what they use, no matter how many database clusters they create and deploy. 

Carl Olofson, research vice president at IDC, noted: “A core benefit of the cloud is dynamic scalability, but this has been more difficult to achieve for storage than with compute. By decoupling compute from storage, DataStax’s Astra DB service lets users take advantage of the innate elasticity of the cloud for data, with a cloud agnostic database.”

While Cassandra is more than a decade young, it is a database for today.  If the argument of 2010 was “Cassandra may be the future,” and 2017 “Cassandra is mature,” the 2021 version is “Cassandra is an essential part of any modern data platform.” The developments in Cassandra and its surrounding ecosystem point to a coming wave of new developers and enterprises worldwide for whom Cassandra is not just a sensible choice, but an obvious one.

Want to learn more about DataStax Astra DB, built on Apache Cassandra? Sign up for a free demo.

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Couchbase Advances Case for Becoming Your System of Record

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Posted on nosqlgooglealerts. Visit nosqlgooglealerts

(Joe Techapanupreeda/Shutterstock)

By making its database look and behave more like a traditional relational database with today’s launch of Couchbase Server 7.0, Couchbase is giving companies a reason to leave their Oracle systems and power their most important transactions with its NoSQL database.

While there are over 30 new features in Couchbase Server 7.0, there are really two that stand out: support for multi-document SQL transactions, and the addition of scopes and collections in the schema-less JSON document store.

Couchbase CTO Ravi Mayuram gave Datanami the low-down on what the updates mean for customers. Let’s start with support for multi-document SQL transactions.

Multi-Document SQL Transactions

Since Couchbase is a document-oriented database, the data ultimately is stored in a series of JSON documents. That is not changing with this release, although the way that companies can ultimately manipulate that JSON data–through SQL–is changing.

From the very beginning, the Couchbase database has supported single-document ACID transactions. This provided developers the capability to retrieve data using PUTS and GETS through the standard API. They could also use Couchbase’s N1QL query language, which exposes a SQL syntax to the outside but connects directly to the document store on the inside, to fetch data from the JSON documents.

With the launch of Couchbase Server 6.5 in early 2020, the company (which went public last week) added support for multi-document ACID database transactions. This provided customers with the confidence to push more of the complexity inherent with serving transactions onto the database itself, instead of requiring the developers to account for it in the application. Again, this primarily impacted API access to the database.

Now with 7.0, Couchbase has added multi-document SQL transactions to N1QL while adhering to the ACID precepts of atomicity, consistency, isolation, and durability that has been the standard for transaction-oriented databases for decades. This is a significant change because it allows customers to take the SQL statements they have already developed for a variety of existing applications, and run them straight against the Couchbase database, Mayuram says.

“Earlier, you could get to the JSON documents using the API–GET, SET, UPDATE, that kind of stuff,” he says. “Now [you can do] the data manipulation part using SQL transactional semantics.”

Couchbase Server 7.0 brings a number of new features (Source: Couchbase)

The update is important, Mayuram says, because it allows Couchbase Server to handle the complexity inherent with active transactional systems in the same manner that customer have grown accustomed to with Oracle, SQL Server, and Db2 databases, but without giving up the flexibility that’s inherent with a distributed, schema-less database.

The hard part for Couchbase developers was to expose the transactional semantics and maintain those ACID guarantees while the database underneath is being constantly hammered by API requests, N1QL queries, and now SQL statements.

“Your schema is changing, new tables are being created, some tables are being updated, something is being deleted, we’re adding more capacity, and the data is actually being moved– yet the transactional guarantee is maintained for you,” he says. “The system could be in tumult underneath, yet we manage it for you.”

Couchbase offers a more in-depth description of SQL transactions in its blog.

Scopes and Collections

The second major new feature–the addition of scopes and collections–is important for a similar reason.

In a relational database, the ontology of the data structure, from general to specific, goes as follows; database to schema to table to row to column. This is how database administrators and developers are accustomed to thinking about their data.

In Couchbase, the ontology has been much simpler. According to Mayuram, there was the database, a bucket, and a document. “And we are schema-less, so it did not have to be anything more complicated than that,” he said.

Things are getting a little more nuanced in Couchbase Server 7.0 with scopes and collections, which directly correspond with schemas and tables.

“In the Couchbase ontology now, you have bucket, then you have scope, then you collections, then you have documents, and inside the documents, you have keys,” Mayuram says. “So there is one-to-one mapping…so people don’t have to think too much to figure out, how do I map this?”

Bringing It Together

Ultimately, support for multi-document SQL transactions and scope and collections are important because they lower the amount of development work required to move from a relational database to the document database, while simultaneously increasing the familiarity that developers feel towards the NoSQL database, Mayuram says.

“If you know how to drive a car, you can give them a Tesla [and they can drive it],” he says. “It’s just when you open the hood, you see all the differences. There is no internal combustion engine, none of that stuff. That’s what we want to achieve, because underneath the covers, it’s a totally different system. A document-based system is very different from a relational, tuple-based system. But we have put the rigor behind it in terms of using the right computer science theory, with the generalized relational algebra and the distributed transaction work we had to do to get that right.”

In the past, Couchbase spoke of being the database for the systems of engagement, those new Web and mobile applications that power all the user interactions that occur before the user is read to hit the “buy” button and complete a transaction, for example. The ratio of these interactions to a traditional transaction was 1,000 to 1, the company said.

Since traditional databases were ill-equipped to handle that volume, they adopted NoSQL systems like Couchbase to handle them. Yet the stakes were much higher with that credit card transaction, and so customers kept the traditional relational databases, which offered the ACID guarantees that they (and their banks) needed.

Now that Couchbase can offer those guarantees–it has also adopted the Raft consensus algorithm to keep data in synch as it moves between geographically distributed nodes–the company is targeting the more valuable transactions. It expects the volume of migrations off Oracle to its NoSQL database to accelerate because of it.

“This gives you the transactional guarantees to become the system of record for enterprise-grade applications with the least amount of effort, or without requiring you to reprogram the application,” Muyuram says. “We give you those familiar guarantees, and the necessary guarantees to say your data is going to sit in the disk, no matter what happens around that. That’s the guarantee you actually need for you to become a system of record.”

Related Items:

Couchbase Pops in Stock Market Debut

There’s a NoSQL Database for That

Couchbase Nabs $105M as it Readies Cloud Offering

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GitHub Funds Independent Legal Support for Developers Against DMCA

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MMS Sergio De Simone

Article originally posted on InfoQ. Visit InfoQ

GitHub has launched a program to offer developers free legal support from Stanford Law School against DMCA takedowns requested under Section 1201.

According to GitHub’s Head of Developer Policy Mike Linksvayer, DMCA complexity is such that especially open source developers may tend to just removing allegedly infringing code without even trying to defend their rights. To change this, GitHub has created the GitHub Developer Rights Fellowship at the Stanford Law School Juelsgaard Intellectual Property and Innovation Clinic.

Funded with one million dollar, this initiative will allow GitHub to give developers the option to defend their right when receiving a takedown notification at no cost.

At the moment, GitHub Developers Rights Fellowship will only cover Section 1201 of the Digital Millennium Copyright Act, which focuses on the circumvention of technological protection measures like digital rights management (DRM), but Linksvayer says GitHub might consider to offer referrals for other situations as they learn from experience and where possible.

InfoQ has taken the chance to speak with Mike Linksvayer, Head of Developer Policy at GitHub, and Phil Malone, Director of Juelsgaard Clinic, Stanford Law.

InfoQ: Could you provide some figures regarding how many takedown notices GitHub receives, and how many of them end up in eventually re-enabling the allegedly infringing content?

Linksvayer: GitHub does not report on the number of takedown notices it receives, however last year we received and processed over 2000 valid DMCA takedown notices, as detailed in our transparency report.

InfoQ: What kind of content gets flagged by the copyright owner as infringing on their copyright? And what kind of infringement claims are the most common?

Linksvayer: In the interest of transparency, we share every DMCA takedown notice we process.

The DMCA takedowns that GitHub processes tend to be about non-code content. Takedown notices about open source license non-compliance are relatively rare as that’s the sort of thing that can typically be worked out amicably. Again, full detail can be found in our transparency report.

InfoQ: Could you provide some recent examples where, had the Developer Defense Fund already been in place, it would have helped developers to defend their rights?

Linksvayer: In general, most claims based on circumvention are complicated and often involve novel legal issues. That is why GitHub has decided to create the Developer Defense Fund to more broadly help with this category of cases. Some examples can include cases involving security research, or reverse engineering by fans who wanted to play an old favorite game, or cases involving hobbyists who create unofficial interfaces to tinker with hardware.

InfoQ: What is your general appreciation of the way DMCA takedowns have been working for software artifacts?

Malone: I agree with what Mike says above that relatively few DMCA takedowns for code are related to open source license noncompliance. But as with the DMCA generally, takedowns focused on Section 1201 vary a lot in quality. Some are legitimate and target code that actually violates, or enables others to violate, the anti-circumvention restrictions of Section 1201. Others may be completely unfounded and abusive, intended to take down code projects for illegitimate reasons. And many others are somewhere in between — not necessarily sent in bad faith or abusive but nevertheless legally questionable or misplaced.

Unfortunately, software developers who are on the receiving end of DMCA notices often don’t have the specialized knowledge or resources to properly analyze the notices and decide whether or not they are valid, and to decide how to respond. The Fellowship is intended to give developers more information, resources, and assistance so they can respond appropriately under the circumstances.

InfoQ: What are the practices that make it work at best or at its worst (e.g., automated takedowns)?

Malone: Automated takedowns are extremely common in the context of DMCA Section 512 takedowns but less so with Section 1201 takedowns. But in either situation they can cause serious problems with how the DMCA’s notice and takedown process works.

Automated takedowns are typically very bad at assessing the context in which particular code or content is used. As a result, they often ignore legitimate justifications a developer or creator may have under the law for their code or content, such as copyright exceptions like fair use or carve-outs to Section 1201. Without human judgment in the loop to examine the circumstances of a particular case, DMCA notices are often sent that are not legally justified because they don’t take the context into account.

InfoQ: What would you change of DMCA to make it more effective and just, less prone to misuse?

Malone: Improper DMCA takedown notices are a real and significant problem. For example, many takedown notices are directed at code or content that is not actually infringing, or for which the person who sends the notice is not legally entitled to enforce. In the worst cases, some people misuse the takedown process to damage code development projects or hurt competitors, or to remove speech or other content that they don’t like.

One of the most important things currently missing in the DMCA is a meaningful, enforceable mechanism to stop people from this misuse, to deter them from sending takedown notices that they know or should know are not legally justified.

Right now, Section 512(f) of the DMCA allows the recipient of an illegitimate takedown notice to seek damages and attorneys fees from someone who sends a notice that “knowingly materially misrepresents” that particular code or content infringes copyright. But because courts have interpreted Section 512(f) narrowly, it is very difficult in practice to hold senders of improper notices accountable or to obtain a meaningful remedy even if they do. As a result, many people and businesses continue to send improper takedown notices with little worry that they will suffer consequences for their misuse of the DMCA.

Section 512(f) should be strengthened to provide realistic and meaningful penalties and create a real deterrent for anyone misusing the DMCA notice and takedown process.

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