This approach allows vector similarity search engines to ascertain similarities between data of any structure variation, from almost any source, utilizing any data model or schema, to find answers and draw conclusions that other databases can’t.

This technology serves as the backbone to robust applications of generative AI, as it helps organizations leverage “foundation” models and other large language models (LLMs) and contextualize them within an organization’s industry, business model, and data. This is particularly the case in the context of the RAG concept, making LLM responses more tailored, with far fewer “hallucinations.” As such, it’s the bridge between an organization’s data, documents, and policies on the one hand and natural language queries and responses on the other.

These natural language interactions are swiftly becoming the de facto means of interfacing with data assets, as well as drastically lowering the skills required to query and understand data for any specific business purpose. Thus, the burgeoning target audience for these vector database computing platforms includes all enterprise personas, from the C-suite to business end-users. Moreover, a vector database’s utility expands to almost any enterprise application via the access they afford organizations to their data.

Despite some prominent use cases, the adoption of vector databases and similarity search engines is still in its nascent stage. The capabilities found in these computing platforms are undergoing a rapid shift, and the heretofore specialized space is encountering consolidation with other more mainstream, general-purpose databases. Vector database technology is now incorporated into a number of different solutions for everything from process automation to streaming data platforms and relational databases. Consequently, there’s impressive diversity found among the vendors, from open source to closed source offerings and from pure-play vector database platforms to other types of databases that have been enhanced with new vector capabilities.

In fact, vector similarity search functionality may be in the process of being incorporated into virtually all database platforms for storing and retrieving unstructured data. Certainly, we expect to see a greater understanding of vector technology and expectations for it by vendors in this year’s report as compared to last year. This development is reflected in the increased availability of lexical and hybrid search in addition to vector search and accommodations for multimodal search across text, image, and even video and audio content. As the market continues to mature, the demand for these capabilities will almost certainly increase.

This is the second year that GigaOm has reported on the vector database space in the context of our Sonar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Sonar report provides an overview of the market’s vendors and their available offerings, outlines the key characteristics that prospective buyers should consider when evaluating solutions, and equips IT decision-makers with the information they need to select the best solution for their business and use case requirements.

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The importance of data catalogs is manifold. They provide a bridge for users of all types—specifically technical and business users—to comprehend the meaning of data and its relevance to business goals, such as meeting objectives for customer service or sales. Consequently, data catalogs couple the technical and business descriptions of data, including statistical data profiling information, hierarchies of terms, and business glossary definitions of data in a single place to clarify data’s utility. Data catalogs also function as hubs for elucidating, simplifying, and deconstructing the data intelligence pillars.

Through interactive views and detailed verbal and statistical information, data catalogs allow users to drill down into specifics of schema, data lineage, data quality scores and metrics, business rules, data retention policies, and more. Additionally, they serve as collaborative hubs with constructs for commenting, questioning, ranking, grading, and assessing specific datasets. These capabilities apply across different business domains, use cases, data types, and organizations. Consequently, these vital metadata repositories boost data literacy, broaden the adoption of data-driven processes, and propagate data culture more than any other infrastructural component does.

In addition to servicing each user persona within an organization—whether upper-level management, business users, IT teams, admins, data scientists, or data engineers—data catalogs facilitate collaborative data engagement. They have become increasingly critical to enterprises offering a centralized platform for making sense of and leveraging the expanding data landscape.

With each new implementation of data mesh, data products, data fabric, hybrid cloud, and multicloud deployment, the need to co-locate information about assets across these architectures grows. Data catalogs are quickly becoming control planes for each of these architectures and helping manage the mounting decentralization taking root across the data ecosystem.

This versatility effectively trumps even the individual features, automation points, and AI-driven enhancements that make data catalogs increasingly easy to use. Data catalogs have long provided the common platform on which the facets of data intelligence, as well as numerous enterprise disciplines and data sources, depend for the sustainable, long-term reuse of enterprise data.

Now, data catalogs are poised to become control planes for these resources, involving everything from governed data access to aspects of data pipeline management and more.

This marks our fourth year evaluating the data catalog space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 18 of the top data catalog solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading data catalog offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

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To meet the demand to process large volumes of data in real time, businesses have a choice. Deploy individual capabilities to collect, manage, and process data or invest in a streaming data platform that covers these activities from end to end.

Integrated platforms provide extremely fast processing and analytics on streaming data sources like website clicks, sensor data, Internet of Things data, and continuously generated machine data. They solve the challenge of transforming largely unstructured and semi-structured data into a consumable format for analyzing and acting on that data. The best ones do so in real time, a term typically referring to completing a task or function within the time specified for doing so in a business SLA.

Hazelcast’s real-time stream processing platform utilizes a fast data store and real-time stream processing capabilities that are integrated with MLOps to provide in-the-moment action for fleeting business opportunities. Its low latency and extreme responsiveness allow organizations to achieve business objectives and monetize data that would otherwise not be possible.

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Data security and access governance has surged as a primary concern for all organizations, regardless of industry or focus. Data privacy regulations—and the demand for consumer rights—are increasing at the international, national, and local levels. The rate and severity of data breaches are also growing, along with the number of valued data sources external to the enterprise. Additional pressures include business demands for faster time to insight for analytics, which are often unmet because of privacy and security concerns that delay or prohibit users from accessing necessary data.

Data security and access governance alleviate these issues by providing timely, secure data access that complies with regulations, enhances internal security, and fulfills business needs. By providing a single pane of glass for governing distributed data sources and enforcing access policies within source systems, Privacera excels at solving these challenges. The solution automates vital aspects of discovering, classifying, and tagging sensitive data while providing obfuscation methods to protect data with features for monitoring, alerting, auditing, and issuing reports for data access.

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For example, specialist vendors will likely appeal to organizations with business processes pertaining to quintessential streaming data use cases such as stock trading, fraud detection, and e-commerce, as well as location, process, or health monitoring. Those with applications involving the industrial internet, the internet of things (IoT), edge computing, online advertising or adtech, web analytics, and financial markets may gravitate toward these solutions. For these use cases, data streaming is the principal means of ingesting data, messaging and event processing are the principal means for sharing data, and data streams are often the targets for landing the data.

The other types of vendors highlighted in this report offer streaming data capabilities within a wider ecosystem of product offerings, approaches, and methodologies. For instance, enterprise data management vendors incorporate their streaming data technologies alongside components for data quality, information governance, and data cataloging. With these solutions, streaming data tooling is simply one part of a larger framework.

Cloud vendors reviewed in this report tend to couple their streaming data services tightly with their offerings for storage, databases, and analytics applications. Data platform vendors typically ingrain their streaming data utility into tools for data lakes and ML. It’s essential for buyers to be aware of these differences in the way vendors position their streaming data solutions so they know how well those vendors will meet their requirements. Similarly, this foresight helps to prepare for specific use cases by revealing additional components with which organizations will need to integrate—as well as how to do it.

We review each data streaming platform included in this report against the same capabilities (key criteria) and non-functional requirements (evaluation metrics). Thus, despite the points of distinction in their viability for specific applications, these platforms are objectively reviewed in as much of an “apples-to-apples” comparison structure as is possible. How they measure up for specific organizations depends on those organizations’ use cases, their existing infrastructure, architecture of choice, and long-term and short-term goals.

This GigaOm Radar report highlights key streaming data platform vendors and equips IT decision-makers with the information needed to select the best fit for their business and use case requirements. In the corresponding GigaOm report “Key Criteria for Evaluating Streaming Data Platforms,” we describe in more detail the key features and metrics that are used to evaluate vendors in this market.

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These solutions handle some of the most demanding workloads organizations undertake in terms of data quantities, speed, and variation. Commonplace use cases include sensor data from the internet of things (IoT) and industrial internet sources. Web applications (like ad tech, e-commerce, and gaming) often involve streaming data, as does rapid processing of log files, media and entertainment streaming services, and more. Additionally, the advent of 5G internet connectivity, the proliferation of mobile devices like smartphones and tablets, and the influx of sensor data reinforce the need for real-time interactions.

Continuous data streaming requires an updated paradigm compared to the conventional batch methods that once typified analytics deployments. Today, batch jobs of historical data are regularly aggregated with streaming data for comprehensive analysis, customer 360s, real-time offers, and other low-latency processing activity. Therefore, the onus on streaming data platforms is twofold. They must integrate with conventional analytics while also ingesting, transforming, enriching, classifying, and analyzing real-time data streams. The number of sources involved in these tasks is significant and, for some use cases, is growing alongside that of the data volumes. Conversely, the window of opportunity for acting on the resulting insight, such as for denying a fraudulent transaction before it goes through, is shrinking.

There are several open source options that help with these workloads. However, proprietary streaming data platforms supplement these options with a number of built-in features for making intelligent inferences from data, curating data, and enhancing data with enterprise levels of consistency and security. Thus, these platforms are gaining credence for their ability to improve decision-making, their analytics sophistication, and the range of data sources they can access for an increasing number of use cases.

Prospective buyers should understand what features and capabilities are typically found in streaming data platforms as well as the vital points of distinction among them. Applying this information to organizational objectives, specific use cases, budgetary concerns, and the technical aptitudes of the current and future user base reveals which offerings are most appropriate.

The GigaOm Key Criteria and Radar reports provide an overview of the streaming data platforms market, identify capabilities (table stakes, key criteria, and emerging technology) and non-functional requirements (evaluation metrics) for selecting a streaming data platform solution, and detail vendors and products that excel. These reports will give prospective buyers an overview of the top vendors in this sector and will help decision-makers evaluate solutions and decide where to invest.

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Data science solutions provide a comprehensive environment in which to manage all aspects of data science—from onboarding data for model training to deploying predictive models.

In this GigaOm Radar report, we review several types of data science vendors. Some focus exclusively on this discipline, whether they’re established players or startups. Others are offerings from major cloud service providers—some of whom are known trailblazers in contemporary AI. There are also enterprise incumbents that have solutions for numerous dimensions of data management, in which their data science solution is part of a broader ecosystem of products.

The versatile nature of the vendors and their products and services makes them suitable for organizations of varying size, technological sophistication, and spending power. This diversity is also ideal for accommodating an array of industries, some of which tend to rely on data science more than others—for example, financial services, life sciences, and e-commerce. The range of use cases supported by the different types of vendors includes everything from customer interactions via natural language processing (NLP) to prescriptive analytics for preventive maintenance.

This GigaOm Radar report highlights key data science vendors and equips IT decision-makers with the information needed to select the best fit for their business and use case requirements. In the corresponding GigaOm report, “Key Criteria for Evaluating Data Science Solutions,” we describe in more detail the key features and metrics that are used to evaluate vendors in this market.

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Many aspects of data science solutions are designed to help organizations deal with the scarcity of these valued professionals—particularly data scientists. There’s an enterprise need to liberate data scientists from manual, ad hoc work (like data preparation) that monopolizes their time and distracts them from creating innovative business solutions. Traditionally, such grunt work was performed in siloed environments specific to individual data scientists. Thus, it often suffered from a lack of consistency, code reuse, data governance, and collaboration.

Data science solutions were devised to streamline these processes, making them repeatable, more transparent, and more productive than the previous disjointed efforts were. These solutions service the full spectrum of the data science lifecycle, which includes data preparation, model training, feature engineering, testing, and deployment. There’s also a regular monitoring and recalibration cycle for ensuring models operate in production as they were designed to, in notebooks and data science sandboxes.

By integrating with common frameworks used in data science (including tools for coding, ML libraries, and resources for scoring and deploying models), such solutions considerably expedite work in this discipline. They also make the process less siloed while helping it conform to conventional enterprise standards for data governance and security. Consequently, these tools assist organizations in scaling their data science efforts to match the demands of contemporary business needs for AI and its immense data quantity requisites. Granted, the value of open source tooling impacting this space is considerable. However, commercial data science solutions command a significant amount of this space to formalize the varying steps required in this field to enhance its efficiency and solidify its worth.

The assessment of the multitude of vendor offerings on the market for data science takes several factors into account. The most notable of these include the range of employee skills, organizational use cases, attendant applications, budgetary concerns, and mission-critical objectives.

The GigaOm Key Criteria and Radar reports provide an overview of the data science market, identify capabilities (table stakes, key criteria, and emerging technology) and non-functional requirements (evaluation metrics) for selecting a data science solution, and detail vendors and products that excel. These reports give prospective buyers an overview of the top vendors in this sector and will help decision-makers evaluate solutions and decide where to invest.

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