Month: June 2023

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Key Points

• GitLab posted a solid quarter supported by demand for AI-powered services. 
• The guidance is above consensus and may get lifted later in the year. 
• The analysts haven’t said much, but revisions are coming, and they may help this market complete its reversal. 
• 5 stocks we like better than GitLab

Unsurprisingly, GitLab (NASDAQ:GTLB) posted a solid quarter and guided the market higher. The company has been gaining traction, and results from MongoDB (NASDAQ:MDB) foreshadowed the news. MongoDB said that it was well-positioned to benefit from the rise of AI because of its developer tools, including interoperability with Gitlab.

What is surprising is that GitLab’s shares surged 30% in premarket trading. The move was largely driven by short-covering, and short sellers may continue influencing the market. The takeaway is that GitLab shares have finally hit bottom, and the time to “git” on board will be soon at hand. With an addressable market of $40 billion and GitLab with less than $0.5 billion in annual revenue, inventors have quite an opportunity.

“With AI revolutionizing how companies develop, secure, and operate software, we believe GitLab is positioned as the leading AI-powered DevSecOps platform,” said Sid Sijbrandij, GitLab CEO and Co-Founder. “Today, we deliver more AI-powered capabilities to customers than any other DevSecOps platform.

GitLab Raises The Roof For Revenue And Earnings 

GitLab had a strong quarter with revenue of $126.88 million, growing 45.2% compared to last year and outpacing consensus by 760 basis points. The gains were driven by growth in clients of all sizes, with those contributing more than $5K in ARR growing by 43% and those contributing more than $100K in ARR by 39%. This is compounded by a 128% net retention rate showing deepening penetration of existing customers as clients rely more heavily on GitLab services. 

The margin news is also impressive. The gross margin was relatively flat compared to last year and strong at 89% GAAP and 91% adjusted. The impressive news is that the operating margin improved by 1700 basis points due to reprioritization to focus on customer needs and internal efficiencies. The takeaway is that adjusted EPS of -$0.06 narrowed sharply compared to the prior quarter and year, beating the Marketbeat.com consensus by $0.08 or 5700 basis points. 

The best news is that business momentum continues to build, and the guidance was raised because of it. The company expects Q2 and FY 2024 revenue and earnings in a range with the low ends above the consensus figures. This is robust guidance and may be cautious, given the appetite for AI development in the economy. Investors might assume the guidance will be increased later this year, which would be another catalyst for higher share prices.

The Sell-Side Put A Bottom In GitLab 

No analysts issued an update immediately after the Q1 results. Still, the trend in sentiment leading into the report and the institutional activity is consistent with a bottom forming in the market. On the analyst end, they have the stock pegged firmly at Moderate Buy, and the price target appears to have bottomed. On the institutional end, their activity is strongly bullish, with buyers outpacing sellers every quarter since the IPO and activity in 2023 has picked up. They own about 50% of the stock and buy at a rate greater than 3:1 versus sellers. 

The chart favors a bottom at the $30 level. That is consistent with post-IPO lows and is confirmed by the post-Q1 2024 EPS release. The short interest may cause volatility in the near term, but that should give way to a sustained rally, given the outlook for revenue growth and profitability. If the stock can rise from these levels, the next target for resistance is near the analysts’ consensus of $56.60, about 20% above the action. 

Should you invest $1,000 in GitLab right now?

Before you consider GitLab, you’ll want to hear this.

MarketBeat keeps track of Wall Street’s top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on… and GitLab wasn’t on the list.

While GitLab currently has a “Moderate Buy” rating among analysts, top-rated analysts believe these five stocks are better buys.

The post It Is Not Too Late To “Git” On Board With GitLab appeared first on MarketBeat.

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

Posted on mongodb google news. Visit mongodb google news

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

Posted on mongodb google news. Visit mongodb google news

MarketQuest.biz has recently conducted a study on Global Non-relational SQL Market for the forecast period of 2023 to 2029. The study has been conducted based on several qualitative and quantitative information. This information is crucial for the market participants who are entering the Non-relational SQL market. The information is based upon the insights gathered from primary and secondary source of data.

Primary sources of data are sourced from surveys and interviews with industry experts, consultants, product manufacturers and managers, suppliers VPs, executing managers, etc.  Secondary data sources include case studies, financial statements, annual reports, articles, white papers, press releases, paid data sources and research studies. Thus, the Non-relational SQL market summarized all the quantitative and qualitative data necessary for forming the analysis.

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The Non-relational SQL market is based upon segmentation analysis. These segments are categorized on the basis of:

Type of the products:

• Key-Value Store
• Document Databases
• Column Based Stores
• Graph Database

Application of the products:

• Data Storage
• Metadata Store
• Cache Memory
• Distributed Data Depository
• e-Commerce
• Mobile Apps
• Web Applications
• Data Analytics
• Social Networking

The Non-relational SQL market also includes the information regarding key participants. Some of these market players include:

• Microsoft SQL Server
• MySQL
• MongoDB
• PostgreSQL
• Oracle Database
• MongoLab
• MarkLogic
• Couchbase
• CloudDB
• DynamoDB
• Basho Technologies
• Aerospike
• IBM
• Neo
• Hypertable
• Cisco
• Objectivity

Apart from this, the analysis of the study is based upon the regions which are further categorized into the following countries:

• North America (United States, Canada and Mexico)
• Europe (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe)
• Asia-Pacific (China, Japan, Korea, India, Southeast Asia, and Australia)
• South America (Brazil, Argentina, Colombia, and Rest of South America)
• Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Rest of Middle East & Africa)

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https://www.marketwatch.com/press-release/ytterbium-metal-market-2023—in-depth-research-with-emerging-growth-major-manufacturers-industry-share-and-forecast-to-2029-2023-05-21

https://www.marketwatch.com/press-release/silicone-hygienic-diaphragm-valve-market-trend-analysis-and-revenue-growth-over-forecast-of-2023-2029-2023-05-21

https://www.marketwatch.com/press-release/fiberglass-electrical-enclosure-market-2023—key-drivers-top-countries-data-with-cagr-value-qualitative-outlook-and-forecast-by-2029-2023-05-21

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<meta name="x-tns-categories" content="AI / Data / Machine Learning“><meta name="x-tns-authors" content="Chris J. Preimesberger“>

2023-06-07 10:07:25

DataStax Adds Vector Search to Astra DB on Google Cloud

DataStax is working with the Google Cloud AI/ML Center of Excellence as part of the Built with Google AI program to enable Google Cloud’s generative AI offerings to improve the capabilities of customers using DataStax.

Jun 7th, 2023 10:07am by

Chris J. Preimesberger

With so much data piling up everywhere, loaded database nodes are becoming a serious challenge for users to search faster and more accurately to find what they are seeking.

DataStax, which makes a real-time database cloud service built upon open source Apache Cassandra, announced today that its Database as a Service (DBaaS), Astra DB, now supports vector search. This is fast becoming an essential capability for enabling databases to provide long-term memory for AI applications using large language models (LLMs) and other AI use cases.

DataStax is working with the Google Cloud AI/ML Center of Excellence as part of the Built with Google AI program to enable Google Cloud’s generative AI offerings to improve the capabilities of customers using DataStax.

Vector search can be difficult to explain to non-mathematics-type people. It uses machine learning to convert unstructured data, such as text and images, into a numeric representation within the database called a vector. This vector representation captures the meaning and context of the data, allowing for more accurate and relevant search results. It also is able to recognize and connect similar vectors in the database within the context of the query in order to produce more accurate results.

Vector search is often used for semantic search, a type of search that looks for items that are related in meaning, rather than just those that contain the same keywords. For example, a vector search engine could be used to find songs that are similar to a user’s favorite song, even if they don’t share any of the same keywords.

‘Vector Search Is Magic’

“Vector search is magic because it understands what you meant vs. what you said (in a query),” DataStax CPO Ed Anuff told The New Stack. “The more complex a piece of content is, turning it into a vector becomes a much more efficient way of finding this similarity without having to try to guess which keywords are (exactly) right.

“Let’s imagine that I have a database of all of the articles you’ve written. The process of turning each one of your articles into a vector is done through an LLM (large language model), and it looks through the entirety of each article. It figures out what are the most important pieces of an article, and the vector that it produces gets to the essence of it in a concise way. For example, even though you might have used the word ‘Cassandra’ many times in an article, it knows the LLM when it transforms into the vector. It knows that your article is about an open-source database – not about the Cassandra constellation or a performance artist named Cassandra,” Anuff said.

Developers create vectors with simple API calls, and they query those vectors on simple API calls. “But they can now put this powerful capability to work. So that’s why vectorization is such a powerful aspect of this,” Anuff said.

Some of the benefits of using vector databases include:

• Scalability: They can scale to handle large amounts of data.
• Flexibility: They can be used to store and manage a variety of data types, including structured, unstructured and semi-structured data.
• Performance: They can provide high performance for queries on large datasets.

Vector search is also used for image search. In this case, the vectors represent the features of an image, such as its color, texture, and shape. This allows for more accurate and relevant image search results, such as finding images that are similar to a user-uploaded image.

DataStax is launching the new vector search tool and other new features via a NoSQL copilot — a Google Cloud Gen AI-powered chatbot that helps DataStax customers develop AI applications on Astra DB. DataStax and Google Cloud are releasing CassIO, an open source plugin to LangChain that enables Google Cloud’s Vertex AI service to combine with Cassandra for caching, vector search, and chat history retrieval.

Designed for Real-Time AI Projects

Coming on the heels of the introduction of vector search into Cassandra, the availability of this new tool in the pay-as-you-go Astra DB service is designed to enable developers to leverage the massively scalable Cassandra database for their LLM, AI assistant, and real-time generative AI projects, Anuff said.

“Vector search is a key part of the new AI stack; every developer building for AI needs to make their data easily queryable by AI agents,” Anuff said. “Astra DB is not only built for global scale and availability, but it supports the most stringent enterprise-level requirements for managing sensitive data including HIPAA, PCI, and PII regulations. It’s an ideal option for both startups and enterprises that manage sensitive user information and want to build impactful generative AI applications.”

Vector search enables developers to search by using “embeddings”; for example, Google Cloud’s API for text embedding, which can represent semantic concepts as vectors to search unstructured datasets, such as text and images. Embeddings are tools that enable search in natural language across a large corpus of data, in different formats, in order to extract the most relevant pieces of data.

New Capabilities in the Tool

In addition, DataStax has partnered with Google Cloud on several new capabilities:

• CassIO: The CassIO open source library enables the addition of Cassandra into popular generative AI SDKs such as LangChain.
• Google Cloud BigQuery Integration: New integration enables Google Cloud users to seamlessly import and export data from Cassandra into BigQuery straight from their Google Cloud Console to create and serve ML features in real time.
• Google Cloud DataFlow Integration: New integration pipes real-time data to and from Cassandra for serving real-time features to ML models, integrating with other analytics systems such as BigQuery, and real-time monitoring of generative AI model performance.

Goldman Sachs Research estimates that the generative AI software market could grow to $150 billion, compared to $685 billion for the global software industry.

Vector search is available today as a non-production use public preview in the serverless Astra DB cloud database. It will initially be available exclusively on Google Cloud, with availability on other public clouds to follow. Developers can get started immediately by signing up for Astra.

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JEP 404, Generational Shenandoah (Experimental), has been promoted from Proposed to Target to Targeted for JDK 21. This JEP proposes to “enhance the Shenandoah garbage collector with experimental generational collection capabilities to improve sustainable throughput, load-spike resilience, and memory utilization.” Compared to other garbage collectors, such as G1, CMS, and Parallel, Shenandoah currently requires additional heap headroom and has a more difficult time recovering space occupied by unreachable objects.

The Shenandoah garbage collector, utilized in Java applications for its low pause times and latency sensitivity, has been upgraded to include experimental generational collection capabilities. The enhancement is aimed at improving sustainable throughput, load-spike resilience, and memory utilization. The Shenandoah GC can be activated by using the following JVM command line options:

-XX:+UnlockExperimentalVMOptions -XX:ShenandoahGCMode=generational

This will shift Shenandoah into its generational mode. Detailed guidance for configuring and tuning the JVM for optimal operation of applications running with Shenandoah GC in generational mode is available on the project wiki.

The Shenandoah GC is designed to minimize the average GC cost, and the new mode adopts the generational hypothesis that most objects die young. Hence, GC efforts should focus more on dealing with young, short-lived objects. The implementation, currently in its experimental phase, is expected to offer dynamic adaptability for varied workloads.

The enhancement divides the Java heap into two generations: the young and the old. Each generation is composed of a subset of the Shenandoah heap’s regions, with a specific size determined by its occupied regions plus a quota of free regions.

The enhancement doesn’t aim to replace non-generational Shenandoah or improve performance for every possible workload. It isn’t intended to maximize mutator throughput or improve CPU and power usage compared to traditional stop-the-world GCs. Instead, it focuses on reducing the sustained memory footprint, CPU and power usage while increasing throughput and resilience during allocation spikes.

Testing for generational Shenandoah will involve benchmarking against the non-generational version using SPECjbb2015, HyperAlloc, Extremem and Dacapo. Operational envelopes for HyperAlloc, Extremem, and similar workloads will be compared to non-generational Shenandoah, with successful runs aiming to reduce or eliminate the number of allocation stalls and the need for full or degenerated collections.

This enhancement to Shenandoah is seen as a major step forward in garbage collection for Java applications. Its unique Load Reference Barrier (LRB) supports both 32-bit builds and compressed object pointers in 64-bit builds, which the majority of Java heaps are able to take advantage of, making it an efficient and valuable feature.

While the enhancement comes with the risk of increased pause times and mutator overhead due to remembered-set operations, the team is actively refining the algorithms to control collection-phase scheduling, young-generation sizing, tenuring age, and other auto-tuning mechanisms.

The Shenandoah team has clearly stated that this enhancement is in an experimental phase and may require manual tuning for optimal performance. As the system continues to be refined and improved while conducting all development operations in the openjdk/shenandoah repository on the master branch, it is expected to bring considerable performance enhancements to Java applications running with Shenandoah GC. This update places Shenandoah at the forefront of Java garbage collection, showcasing its potential to revolutionize the process.

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MongoDB (MDB Quick QuoteMDB – Free Report) could be a solid addition to your portfolio given a notable revision in the company’s earnings estimates. While the stock has been gaining lately, the trend might continue since its earnings outlook is still improving.

The rising trend in estimate revisions, which is a result of growing analyst optimism on the earnings prospects of this database platform, should get reflected in its stock price. After all, empirical research shows a strong correlation between trends in earnings estimate revisions and near-term stock price movements. Our stock rating tool — the Zacks Rank — has this insight at its core.

The five-grade Zacks Rank system, which ranges from a Zacks Rank #1 (Strong Buy) to a Zacks Rank #5 (Strong Sell), has an impressive externally-audited track record of outperformance, with Zacks #1 Ranked stocks generating an average annual return of +25% since 2008.

Consensus earnings estimates for the next quarter and full year have moved considerably higher for MongoDB, as there has been strong agreement among the covering analysts in raising estimates.

Current-Quarter Estimate Revisions

For the current quarter, the company is expected to earn $0.41 per share, which is a change of +278.26% from the year-ago reported number.

Over the last 30 days, the Zacks Consensus Estimate for MongoDB has increased 33.64% because seven estimates have moved higher compared to no negative revisions.

Current-Year Estimate Revisions

The company is expected to earn $1.45 per share for the full year, which represents a change of +79.01% from the prior-year number.

The revisions trend for the current year also appears quite promising for MongoDB, with seven estimates moving higher over the past month compared to no negative revisions. The consensus estimate has also received a boost over this time frame, increasing 29.58%.

Favorable Zacks Rank

Thanks to promising estimate revisions, MongoDB currently carries a Zacks Rank #2 (Buy). The Zacks Rank is a tried-and-tested rating tool that helps investors effectively harness the power of earnings estimate revisions and make the right investment decision. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.

Our research shows that stocks with Zacks Rank #1 (Strong Buy) and 2 (Buy) significantly outperform the S&P 500.

Bottom Line

Investors have been betting on MongoDB because of its solid estimate revisions, as evident from the stock’s 54.9% gain over the past four weeks. As its earnings growth prospects might push the stock higher, you may consider adding it to your portfolio right away.

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

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Momentum investing revolves around the idea of following a stock’s recent trend in either direction. In the ‘long’ context, investors will be essentially be “buying high, but hoping to sell even higher.” With this methodology, taking advantage of trends in a stock’s price is key; once a stock establishes a course, it is more than likely to continue moving that way. The goal is that once a stock heads down a fixed path, it will lead to timely and profitable trades.

Even though momentum is a popular stock characteristic, it can be tough to define. Debate surrounding which are the best and worst metrics to focus on is lengthy, but the Zacks Momentum Style Score, part of the Zacks Style Scores, helps address this issue for us.

Below, we take a look at MongoDB (MDB Quick QuoteMDB – Free Report) , which currently has a Momentum Style Score of A. We also discuss some of the main drivers of the Momentum Style Score, like price change and earnings estimate revisions.

It’s also important to note that Style Scores work as a complement to the Zacks Rank, our stock rating system that has an impressive track record of outperformance. MongoDB currently has a Zacks Rank of #2 (Buy). Our research shows that stocks rated Zacks Rank #1 (Strong Buy) and #2 (Buy) and Style Scores of A or B outperform the market over the following one-month period.

You can see the current list of Zacks #1 Rank Stocks here >>>

Set to Beat the Market?

In order to see if MDB is a promising momentum pick, let’s examine some Momentum Style elements to see if this database platform holds up.

Looking at a stock’s short-term price activity is a great way to gauge if it has momentum, since this can reflect both the current interest in a stock and if buyers or sellers have the upper hand at the moment. It’s also helpful to compare a security to its industry; this can show investors the best companies in a particular area.

For MDB, shares are up 32.8% over the past week while the Zacks Internet – Software industry is up 3.13% over the same time period. Shares are looking quite well from a longer time frame too, as the monthly price change of 54.88% compares favorably with the industry’s 5.28% performance as well.

While any stock can see its price increase, it takes a real winner to consistently beat the market. That is why looking at longer term price metrics — such as performance over the past three months or year — can be useful as well. Shares of MongoDB have increased 89.38% over the past quarter, and have gained 31.13% in the last year. In comparison, the S&P 500 has only moved 6.17% and 5.54%, respectively.

Investors should also pay attention to MDB’s average 20-day trading volume. Volume is a useful item in many ways, and the 20-day average establishes a good price-to-volume baseline; a rising stock with above average volume is generally a bullish sign, whereas a declining stock on above average volume is typically bearish. MDB is currently averaging 2,317,495 shares for the last 20 days.

Earnings Outlook

The Zacks Momentum Style Score encompasses many things, including estimate revisions and a stock’s price movement. Investors should note that earnings estimates are also significant to the Zacks Rank, and a nice path here can be promising. We have recently been noticing this with MDB.

Over the past two months, 7 earnings estimates moved higher compared to none lower for the full year. These revisions helped boost MDB’s consensus estimate, increasing from $1.03 to $1.45 in the past 60 days. Looking at the next fiscal year, 7 estimates have moved upwards while there have been no downward revisions in the same time period.

Bottom Line

Taking into account all of these elements, it should come as no surprise that MDB is a #2 (Buy) stock with a Momentum Score of A. If you’ve been searching for a fresh pick that’s set to rise in the near-term, make sure to keep MongoDB on your short list.

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

Posted on mongodb google news. Visit mongodb google news

Shares of MongoDB (MDB) have been strong performers lately, with the stock up 54.9% over the past month. The stock hit a new 52-week high of $398.89 in the previous session. MongoDB has gained 96.9% since the start of the year compared to the 33.6% move for the Zacks Computer and Technology sector and the 43.8% return for the Zacks Internet – Software industry.

What’s Driving the Outperformance?

The stock has an impressive record of positive earnings surprises, as it hasn’t missed our earnings consensus estimate in any of the last four quarters. In its last earnings report on June 1, 2023, MongoDB reported EPS of $0.56 versus consensus estimate of $0.19 while it beat the consensus revenue estimate by 6.35%.

For the current fiscal year, MongoDB is expected to post earnings of $1.45 per share on $1.53 billion in revenues. This represents a 79.01% change in EPS on a 19.38% change in revenues. For the next fiscal year, the company is expected to earn $1.97 per share on $1.86 billion in revenues. This represents a year-over-year change of 35.95% and 21.18%, respectively.

Valuation Metrics

MongoDB may be at a 52-week high right now, but what might the future hold for the stock? A key aspect of this question is taking a look at valuation metrics in order to determine if the company has run ahead of itself.

On this front, we can look at the Zacks Style Scores, as these give investors a variety of ways to comb through stocks (beyond looking at the Zacks Rank of a security). These styles are represented by grades running from A to F in the categories of Value, Growth, and Momentum, while there is a combined VGM Score as well. The idea behind the style scores is to help investors pick the most appropriate Zacks Rank stocks based on their individual investment style.

MongoDB has a Value Score of F. The stock’s Growth and Momentum Scores are A and A, respectively, giving the company a VGM Score of B.

In terms of its value breakdown, the stock currently trades at 267.8X current fiscal year EPS estimates, which is a premium to the peer industry average of 42.7X. On a trailing cash flow basis, the stock currently trades at 5X versus its peer group’s average of 17.7X. This isn’t enough to put the company in the top echelon of all stocks we cover from a value perspective.

Zacks Rank

We also need to consider the stock’s Zacks Rank, as this supersedes any trend on the style score front. Fortunately, MongoDB currently has a Zacks Rank of #2 (Buy) thanks to rising earnings estimates.

Since we recommend that investors select stocks carrying Zacks Rank of 1 (Strong Buy) or 2 (Buy) and Style Scores of A or B, it looks as if MongoDB meets the list of requirements. Thus, it seems as though MongoDB shares could have a bit more room to run in the near term.

How Does MDB Stack Up to the Competition?

Shares of MDB have been soaring, and the company still appears to be a decent choice, but what about the rest of the industry? One industry peer that looks good is Rimini Street, Inc. (RMNI). RMNI has a Zacks Rank of # 2 (Buy) and a Value Score of A, a Growth Score of A, and a Momentum Score of B.

Earnings were strong last quarter. Rimini Street, Inc. beat our consensus estimate by 22.22%, and for the current fiscal year, RMNI is expected to post earnings of $0.47 per share on revenue of $425.5 million.

Shares of Rimini Street, Inc. have gained 9.5% over the past month, and currently trade at a forward P/E of 9.46X and a P/CF of 10.4X.

The Internet – Software industry is in the top 43% of all the industries we have in our universe, so it looks like there are some nice tailwinds for MDB and RMNI, even beyond their own solid fundamental situation.

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

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By Patrick McFadin, DataStax developer relations and contributor to the Apache Cassandra project.

Netflix tracks every user’s actions to instantly refine its recommendation engine, then uses this data to propose the content users will love. Uber gathers driver, rider, and partner data in the moment and then updates a prediction engine that informs customers about wait times or suggests routes to drivers in real time. FedEx aggregates billions of package events to optimize operations and instantly share visibility with its customers on delivery status.

These leaders succeed with these real-time AI capabilities in large part because of their ability to aggregate massive amounts of real-time data from customers, devices, sensors, or partners as it moves through applications. This data in turn is used to train and serve machine learning models. These companies act on this data in the moment, serving millions of customers in real time. And they all rely on the open-source NoSQL database Apache Cassandra^®.

Let’s take a look at why Cassandra is the database of choice for organizations building enterprise-scale, real-time AI applications.

The challenges posed by real-time AI

Only 12% of AI initiatives succeed in achieving superior growth and business transformation, according to Accenture. Why? In a nutshell, data scientists and developers have been trying to build the most powerful, sophisticated applications for the next generation of business on complex infrastructure built for the demands of yesterday.

Many traditional AI/ML systems, and the outcomes they produce, rely on data warehouses and batch processing. The result: A complex array of technologies, data movements, and transformations are required to “bring” this historical data to ML systems. This alters and slows the flow of data from input to decision to output, resulting in missed opportunities that can open the door for customers to churn or allow recognized cyber security threat patterns to go undetected and unmitigated.

The velocity, type, and volume of data drive the quality of predictions and the impact of the outcomes. Real-time AI demands large amounts of data to train ML models and make accurate predictions or generate new content very quickly. This requires a high-performance database that can bring ML to the data. You’ve created the right architecture to collect and store your data and the best way to keep costs low is to leverage what you have. The solution to a storage cost problem is not adding more storage; it’s finding ways to process your data in place.

Enter Cassandra

There are various databases that can be used to develop a real-time AI application. Relational databases such as MySQL or PostgreSQL may be user-friendly, but they are not capable of managing the vast amounts of data required for web-scale AI applications. Although open-source data stores like Redis are available, they lack the durability necessary to support AI applications that are intended to form the foundation of a business.

For real-time AI to live to its full potential, the database that serves as its foundation must be:

• highly scalable to manage massive amounts of data
• reliable for continuous data access
• fast enough to easily capture big data flows
• flexible enough to deal with various data types.

Cassandra is an open-source NoSQL database that scales with performance and reliability better than any other. Many companies, like those mentioned above, have transformed their businesses and led their industries thanks to real-time AI built on Cassandra. Why?

Horizontal scalability: As AI applications become more sophisticated, they require the ability to handle ever-increasing volumes of data. Cassandra’s distributed architecture is based on consistent hashing, which enables seamless horizontal scaling by evenly distributing data across nodes in the cluster (a collection of nodes). This ensures that your AI applications can handle substantial data growth without compromising performance, a crucial factor from a statistical perspective.

High availability: The decentralized architecture of Cassandra provides high availability and fault tolerance, which ensures that your AI applications remain operational and responsive even during hardware failures or network outages. This feature is especially important for real-time AI applications, as their accuracy and efficiency often rely on continuous access to data for mathematical modeling and analysis.

Low latency: With real-time AI, signals generated by user activities must be captured at a very high rate; the ability to write this data to a database fast is critical. Cassandra’s peer-to-peer architecture and tunable consistency model enable rapid read and write operations, delivering low-latency performance essential for real-time AI applications.

Unlike many other data stores, Cassandra is designed in a way that doesn’t require disk reads or seeks during the write process, so writing data to Cassandra is extremely fast and provides the freedom to capture incoming signals with ease—no matter how fast they arrive.

It ensures that AI algorithms receive the latest data as quickly as possible, allowing for more accurate and timely mathematical computations and decision-making.

Flexible data modeling: Cassandra’s NoSQL data model is schema-free, which means that the methodology for storing data is far more flexible than alternative databases, making it possible to store and query complex and diverse data types common in ML and AI applications. This flexibility enables data scientists to adapt their data models as requirements evolve without having to deal with the constraints of traditional relational databases.

The Cassandra community

The Cassandra open-source project is built and maintained by a community of very smart engineers at some of the biggest, most-advanced users of AI (Apple, Netflix, and Uber, to name a few) who are constantly modernizing and extending the capabilities of the database. The upcoming Cassandra 5.0 release, for example, will offer vector search, a critical feature that will be a groundbreaking aid to organizations grappling with the massive datasets that accompany AI efforts.

These advantages make Cassandra a reliable foundation for real-time AI applications that need to handle massive volumes of data while ensuring continuous data access, high performance, and adaptability. If your organization aims to leverage AI to its full potential, choosing the right database is a critical step in your journey.

By adopting a scalable and durable solution like Cassandra, you can ensure the successful execution of your AI initiatives, reduce cost, and optimize processing. It’s time to reconsider your data infrastructure and invest in the right technology to fuel your growth. Remember, the success of your AI strategy doesn’t only lie in the complexity of your algorithms but also in the robustness of your data management system.

Join the growing community of businesses pioneering the future of AI with Cassandra. Seize the opportunity today and equip your business to make the most of real-time AI.

Learn how DataStax makes real-time AI possible here.

About Patrick McFadin

DataStax

Patrick McFadin is the co-author of the O’Reilly book “Managing Cloud Native Data on Kubernetes.” He works at DataStax in developer relations and as a contributor to the Apache Cassandra project. Previously he has worked as an engineering and architecture lead for various internet companies.

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DataStax is partnering with Google to bring vector search to its AstraDB NoSQL database-as-a-service in an attempt to make Apache Cassandra more compatible with AI and large language model (LLM) workloads.

Vector search, or vectorization, especially in the wake of generative AI proliferation, is seen as a key capability by database vendors as it can reduce the time required to train AI models by cutting down the need to structure data — a practice prevalent with current search technologies. In contrast, vector searches can read the required or necessary property attribute of a data point that is being queried.

“Vector search enables developers to search a database by context or meaning rather than keywords or literal values. This is done by using embeddings, for example, Google Cloud’s API for text embedding, which can represent semantic concepts as vectors to search unstructured datasets such as text and images,” DataStax said in a statement.

Embeddings can be seen as powerful tools that enable search in natural language across a large corpus of data, in different formats, and extract the most relevant pieces of data, Datastax said.

Vector databases are seen by analysts as a “hot ticket” item for 2023 as enterprises look for ways to reduce spending while building generative AI based applications.

AstraDB’s vector search accessible via Google-powered NoSQL copilot

Vector search along with other updates will be accessible inside AstraDB via a Google-powered NoSQL copilot that will also help DataStax customers build AI applications, the company said.

Under the hood, the NoSQL copilot combines Cassandra’s vector Search, Google Cloud’s Gen AI Vertex, LangChain, and GCP BigQuery.

“DataStax and GCP co-designed NoSQL copilot as an LLM Memory toolkit that would then plug into LangChain and make it easy to combine the Vertex Gen AI service with Cassandra for caching, vector search, and chat history retrieval. This then makes it easy for enterprises to build their own Copilot for their business applications and use the combination of AI services on their own data sets held in Cassandra,” said Ed Anuff, chief product officer at DataStax.

Plugging into LangChain, an open source framework aimed at simplifying the development of generative AI-powered applications using large language models, is made possible due to an open source library jointly developed by the two companies.

The library, dubbed CassIO, aims to make it easy to add Cassandra-based databases to generative AI software development kits (SDKs) such as LangChain.

Enterprises can use CassIO to build sophisticated AI assistants, semantic caching for generative AI, browse LLM chat history, and manage Cassandra prompt templates, DataStax said.

Other integrations with Google include the ability for enterprises using Google Cloud to import and export data from Cassandra-based databases into Google’s BigQuery data warehouse by using the Google Cloud Console for creating and serving machine learning based features.

A second integration with Google will allow AstraDB subscribers to pipe real-time data to and from Cassandra to Google Cloud services for monitoring generative AI model performance, DataStax said.

DataStax has also partnered with SpringML to help accelerate the development of generative AI applications using SpringML’s data science and AI service offerings.

Availability of vector search for Cassandra

AstraDB, built on Apache Cassandra, will arguably be one of the first to bring vector search to the open source distributed database. Currently, vector search for Cassandra is being planned for its 5.0 release, a post by the database community, where DataStax is a member, showed.

In terms of availability, AstraDB’s vector search presently can be used in non-production workloads and is in public preview, DataStax said, adding that the search will be initially available exclusively on Google Cloud and later extended to other public clouds.