MARKET RADAR
GigaOm Radar for Data Virtualization
v 1.0ANDREW J. BRUST AND YIANNIS ANTONIOU | NOV 24, 2020 - 3:49 PM CST
TOPIC: DATA VIRTUALIZATION
GigaOm Radar for Data Virtualization
TABLE OF CONTENTS
Summary
1
Key Criteria and Evaluation Metrics Comparison
2
GigaOm Radar
3
Vendor Insights
4
Analysts’ Take
5
About Andrew Brust
6
About Yiannis Antoniou
7
About GigaOm
8
Copyright
1.
Summary
We live in the era of diverse and disparate data. From data centers and public, private, and hybrid clouds, to end user PCs and fast-expanding ranks of IoT devices, the demand for data has never been higher. Likewise, the complexity around that data has never been higher, and that presents a
challenge. Force users to connect to each of these data sources separately and you will alienate them from data-driven practices. Require IT to coalesce all the data sources into a single repository via complex data pipelines, and you court significant operational fragility and risk.
Enterprises need a way to consolidate disparate data sources logically without forcing them to be physically moved or transformed. They need help too in managing, manipulating, and presenting a unified view of data to the downstream consumer. Years of progress have yielded a solution, in the form of data virtualization platforms.
This GigaOm Radar report analyzes the top data virtualization platforms in the market, weighs the key criteria and evaluation metrics used to assess these solutions, and identifies important technologies to consider for the future. This report gives organizations an overview of the leading data virtualization platforms in the market today, and recognizes platforms that excel in particular categories. It is
intended to appeal both to organizations looking to extend investments in existing platforms as well as to those yet to dip their toe in the data virtualization waters.
HOW TO READ THIS REPORT
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key
product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and
progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor's offering in the sector.
Vendor Profile: An in-depth vendor analysis that builds on the framework developed in the
Key Criteria and Radar reports to assess a company's engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.
2.
Key Criteria and Evaluation Metrics Comparison
Building on the findings from the GigaOm report, “Key Criteria for Evaluating Data Virtualization,” Tables 1 and 2 summarize how each vendor included in this research performs in the areas that we consider differentiating and critical in this sector. The objective is to give the reader a snapshot of the technical capabilities of different solutions and define the perimeter of the market landscape.
Table 2. Evaluation Metrics for Data Virtualization
By combining the information provided in Table 1 and Table 2, the reader should develop a clear sense of the market and the available technical solutions within it.
3.
GigaOm Radar
This report synthesizes the analysis of key criteria and their impact on evaluation metrics to inform the GigaOm Radar graphic in Figure 1. The resulting chart is a forward-looking perspective on all the vendors in this report, based on their products’ technical capabilities and feature sets.
Figure 1: GigaOm Radar for Data Virtualization
The GigaOm Radar plots vendor solutions across a series of concentric rings, with those set closer to center judged to be of higher overall value. The chart characterizes each vendor on two axes—Maturity versus Innovation, and Feature Play versus Platform Play—while providing an arrow that projects each
solution’s evolution over the coming 12 to 18 months.
As you can see in the Radar chart in Figure 1, the Leaders circle plays host to six of the eight products in this report. This reflects both the mature nature of the market and the fact that we chose to focus on the most comprehensive and proven solutions. Also notable: most of the activity in the sector occurs in the Platform Play hemisphere, which again is reflective of the strategic nature of data virtualization platform solutions.
Denodo leads all solutions in this report on the strength of its mature, capable, and well-executed, all-around feature set, while AtScale benefits from being a modern platform with compelling execution and excellent support for analytics. Dremio is a worthwhile contender with a strong open-source base, TIBCO stands out as the most established brand in data virtualization, and Data Virtuality offers
excellent data source connectivity and materialized analytical storage capabilities.
INSIDE THE GIGAOM RADAR
The GigaOm Radar weighs each vendor's execution, roadmap, and ability to innovate to plot solutions along two axes, each set as opposing pairs. On the Y axis, Maturity recognizes solution stability, strength of ecosystem, and a conservative stance, while Innovation highlights technical innovation and a more aggressive approach. On the X axis, Feature
Play connotes a narrow focus on niche or cutting-edge functionality, while Platform Play
displays a broader platform focus and commitment to a comprehensive feature set.
The closer to center a solution sits, the better its execution and value, with top performers occupying the inner Leaders circle. The centermost circle is almost always empty, reserved for highly mature and consolidated markets that lack space for further innovation.
The GigaOm Radar offers a forward-looking assessment, plotting the current and projected position of each solution over a 12- to 18-month window. Arrows indicate travel based on strategy and pace of innovation, with vendors designated as Forward Movers, Fast Movers, or Outperformers based on their rate of progression.
Note that the Radar excludes vendor market share as a metric. The focus is on forward-looking analysis that emphasizes the value of innovation and differentiation over incumbent market position.
4.
Vendor Insights
Actifio Virtual Data Pipeline
Founded in 2009, Actifio is a Waltham, MA company known for its well-regarded copy data
management platform. The company’s main offering, Actifio Virtual Data Pipeline (VDP), puts its focus on virtualizing copy data and the rapid creation of easily accessible virtual data clones.
Actifio VDP specializes in the full lifecycle management of virtual data copies across multiple clouds as well as on-premises. The platform uses incremental data ingestion technology in native format at the data block level for a variety of databases and applications. It then creates and combines virtual copies of all data from these sources and produces multiple virtual clones of the combined data that can be used independently of the source systems. These data copies can be continuously refreshed, and point-in-time virtual snapshots can be created for a variety of uses.
Supported database and application systems include Oracle, SQL Server, DB2, MySQL, MongoDB, PostgreSQL, SAP HANA, and Microsoft Dynamics, among others. A set of REST APIs can also help integrate the technology with several infrastructure management frameworks, such as Splunk and ServiceNow, and CI/CD tools such as Ansible, Puppet, Chef, and Maven, among others.
Deployment options are numerous. Actifio can be deployed on-premises on Windows, Linux, and Unix bare metal servers as well as through VMware and Hyper-V virtual machines. The cloud is also widely supported, with deployments available for the Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM, and Oracle public clouds. The company also offers Actifio Sky, a virtual machine appliance, and Actifio CDS/CDX, a physical appliance cluster. The platform is also available through software partnerships, such as IBM’s Virtual Data Pipeline, and through hardware partnerships, such as the Actifio Database Cloning Appliance powered by Dell Technologies.
Actifio’s approach to data virtualization is different from most of the other vendors in this report as it is concerned primarily with optimized data copying and replication rather than in-place management of data. This architectural approach creates a strong platform optimized for use cases such as production data replication for dev and test; continuous back-up and restore; multi-cloud data portability; data sandbox provisioning for security and compliance; data analytics and science; and more.
The platform should appeal especially to organizations that need easy ways of providing virtualized copies of production data to a variety of constituents without heavy investments in storage
infrastructure or ETL processes. The lack of a semantic layer and advanced data governance and metadata tools means the platform will primarily appeal to infrastructure teams within organizations, for whom this should be a very strong offering.
Strengths: Virtualization of data copies is easy and powerful. Unlimited point-in-time data restores. Native, incremental, and continuous data backup. Optimized for major database infrastructure management needs.
Challenges: Not focused on logical semantic data layer. Appeal to non-infrastructure constituents is below competitors.
AtScale
AtScale, a San Mateo, CA and Boston, MA-based company that was founded in 2013, offers data virtualization products with a focus on business intelligence support. The company’s main offering, AtScale Adaptive Analytics, is designed to support several use cases around BI and analytics and is built on a semantic layer with additional levels of features and engineering on top.
That logical semantic layer at the base of the platform—appropriately called the Universal Semantic Layer by the company—enables an OLAP-inspired design over the source data. This allows decoupling of the underlying physical infrastructure and tech-centric data design from what is being presented to downstream data consumers. Through the use of business-friendly nomenclature and the creation of virtual OLAP cubes, a business-friendly semantic layer is overlaid on the data.
This semantic layer is what all supported tools will see as they connect through AtScale, with the underlying physical infrastructure and design free to change as long as the semantic layer interface remains unaltered. The semantic layer eliminates the need for ETL or any data movement in order to make the data usable in its preferred form. Direct connections to the semantic layer through user-based BI tools, such as Tableau, Excel, Power BI, and Qlik, and through SQL, MDX, and REST interfaces are a key feature of the platform. AtScale provides connectivity to major on-premises and cloud data sources, including SQL Server, Hadoop, Teradata, Amazon RedShift, Amazon S3, Google BigQuery, Snowflake, Azure Synapse, and more.
With the help of AI, AtScale also observes data lineage and user behavior and evolves optimized querying, caching, data virtualization and other general data engineering tasks through a set of features the company calls Autonomous Data Engineering. Finally, the platform also offers a virtual data governance layer that can integrate with external data catalog and metadata tools; virtual data discovery tools; data access controls; and other robust governance features. Deployment is supported on-premises for Linux environments and on the major public clouds through a Linux VM.
AtScale’s strong support for a business-friendly semantic layer and its intelligent data engineering are probably its most compelling features. Coupled with an easy-to-use web-based design interface, multi-cloud support, and strong data source and client integration, the offering should have wide appeal, especially for organizations wanting to create or enhance a self-service data culture. In addition, the decoupling between physical infrastructure and logical layer should make AtScale a great fit for data modernization and migration initiatives.
Strengths: Excellent semantic layer capabilities. Automated data engineering based on observed data and user patterns. Good governance and metadata features.
Challenges: AtScale’s strong adherence to dimensional modeling and a semantic layer is a plus for many enterprise organizations but could be a downside for younger organizations less versed in the
enterprise BI paradigm.
Data Virtuality
Data Virtuality, a company with primary locations in San Francisco and Germany was founded in 2012 and offers a data virtualization solution aimed at data integration and management.
The company’s main platform, Logical Data Warehouse, combines data virtualization and ETL
technologies to create a unified data integration and management solution. The platform can be seen as a federated SQL query engine that can also help cache and materialize query results or data views from disparate data sources onto persistent analytical storage to enable query optimization and enhanced performance.
SQL can be used to combine data across different sources, with the resulting new data model
manipulated and persisted in analytical storage, which can be any standard relational database system. Data characteristics and access patterns are studied to create optimal execution paths within the
analytical storage, and query execution can be pushed down to several supported data systems as appropriate.
The data in the logical data warehouse can be accessed and a semantic layer generated through the company’s Data Virtualization Studio client, available for Windows, Mac, and Linux, as well as through a web-based interface called Web Business Data Shop. Leading BI tools such as Tableau, Power BI, Excel, general ODBC and JDBC interfaces, and a REST API for data consumption are also fully supported. More than 200 connectors to data sources are available from the company, with a wide variety of the most common on-premises and cloud-native databases, file systems, and applications supported.
While the company uses analytical storage as the basis of its logical data warehouse concept, direct connection to the data sources is also available for querying. The analytical storage system can be populated using the company’s Data Replication Component, with full load, incremental data load, and change data capture capabilities all available.
The platform also offers good data governance features around data lineage and metadata
management, and deployment is supported on-premises and in the cloud for Windows and Linux-based environments. The platform is additionally complemented by the self-service Pipes and SQL-based Pipes Professional data pipeline products.
Data Virtuality’s federated query engine uses data virtualization in clever ways, optimizing for analytical use cases and focusing on the technical user with an interesting architecture and well-designed
performance optimizations. While not fully focused on supporting a pure business-centric semantic layer, its analytical storage concept, execution engine, and wide connectivity support should help it win clients that are interested in seamlessly aggregating and connecting to data from a wide range of supported data sources.
Strengths: Materialized analytical storage. Excellent connectivity to data sources. Good data replication and ETL semantics.
Challenges: Business-centric capabilities not on par with those of competitors. Platform experience could be enhanced for non-technical users.
Datameer Spotlight
Datameer, founded in San Francisco in 2009, released the first version of its Spotlight platform (originally known as Neebo) at the end of 2019, making it the youngest platform in this report. The company positions Spotlight as a cloud-native self-service hub that helps unify data across a variety of data sources with a particular emphasis on supporting analytics use cases
Spotlight uses data virtualization concepts to connect to on-premises and cloud-native data sources, and uses indexing to create a searchable metadata catalog. The platform also uses a point-and-click visual interface along with AI-aided suggestions to help users create and publish virtual data models on top of the indexed data.
Particular emphasis is placed on search and discoverability, with tagging, sharing, newsfeeds, and general social engagement and collaboration concepts around data being a core focus of the platform. Caching can be performed both on Spotlight itself as well as on any cloud data warehouse the
platform supports. Query pushdown is supported and a user-created business glossary of assets is also available.
The platform currently offers more than 20 connectors to databases and file stores, such as MongoDB, Amazon Redshift, SQL Server, and Oracle, among others. A new suite of connectors acquired by the company and planned for incremental releases over the next few months will increase this count to more than 200 and include a variety of SaaS apps such as SalesForce and Microsoft Dynamics 365, on-premises sources such as SAP, cloud services such as Twitter and Google ads, and more databases and other big data sources. Spotlight sports an execution engine based on Spark SQL, with an
emphasis on push-down queries to these underlying data sources.
Integration with popular BI/analytical tools is robust, with native support for connecting from Excel, Tableau, Power BI, Qlik, and more. A good graphical point-and-click interface for data prepping and modeling is also present, and deployment to AWS, Azure, and GCP is supported. A fully managed, cloud-native service named Spotlight Cloud is also in the process of being rolled out before the end of 2020. Finally, Datameer is working on enhanced capabilities, such as external catalog integration with Collibra, AWS Glue, Azure Data Catalog, and Google Data Catalog.
The platform is complemented by the Datameer X and Spectrum data pipeline products, which can help bring on-premises and cloud data together and enable discovery, modeling, collaboration, and publishing of analytic assets across an organization.
Spotlight is a promising newcomer in the data virtualization fold, with an interesting social engagement and collaboration mode of operation that is a differentiator among most of its competitors. As such, it should merit a closer look from analytics-focused organizations with data sources on the supported list. With the additional development and deployment plans in motion, especially those around wider data source support and the fully managed deployment option, there should be nothing stopping the platform from competing to become a market contender in the future.
Strengths: Well-thought-out social engagement and collaboration concepts around data. Good user experience. Good self-service data prep and modeling capabilities. Good governance and metadata features.
Challenges: Relative immaturity of the platform in comparison to its competitors, since it has been on the market for less than a year. Data source support needs expansion, something the company is actively working on.
Denodo
Founded in 1999, Denodo is a Palo Alto, CA-based company that is widely considered to be one of the pioneers in the data virtualization space. The company’s main offering, the Denodo Platform, has been around since 2002 and has evolved over the years to offer a wide range of features geared toward supporting several virtualization, analytics, big data, master data management, and data science use cases.
The Denodo Platform offers a comprehensive set of features, including a business-centric semantic layer that the company calls the Universal Semantic Layer; in-memory massively parallel processing, partial pre-computed aggregations, pushdowns and other query-acceleration capabilities; a web-based point-and-click user interface for data modeling; data catalog and metadata management; social
engagement and collaboration features; built-in transformations; ML- and AI-based recommendations for optimizing performance; and data-quality features. In addition, the company has added to the core use cases for data virtualization with built-in support for data science notebooks; strong management tools; as well as automated infrastructure support and templates for cloud deployments.
Connectivity options are extensive, with a combination of JDBC and native support for virtually all on-premises and cloud databases on the market. The company also recently introduced a logical multi-cloud architecture that enables data transformations to be performed at each different public multi-cloud provider before being brought down the wire and virtualized on a central platform server. Finally, a data services layer with support for JDBC, ODBC, ADO.NET, REST, OAuth, SAML, OData, and GraphQL, among others, rounds out the strong programmability and connectivity features.
Deployment is available on-premises for Windows, Linux, and Unix, in the major public clouds, and through Docker container images. Automated infrastructure management, auto scaling, and web-based management tools are available. Denodo Cloud offers prebuilt blueprints for deployments on AWS, Azure, and GCP, and a free Denodo Express version allows test-driving the platform by
Denodo’s extensive feature set, well-thought-out logical architecture, and emphasis on ease of use and cloud support position the company as a leader in the data virtualization market. Organizations
invested in the platform will continue to derive value from the company’s ongoing enhancements, while those embarking on evaluation efforts for new deployments should certainly consider adding the Denodo Platform to their comparison lists.
Strengths: Strong all-around feature set. Business-centric semantic layer. Perception of company as leader in the marketplace.
Challenges: Competitors catching up with the main technology value proposition. Social engagement-based features need more development, an area the company is currently investing in.
Dremio
Dremio, a Santa Clara, CA-based company founded in 2015, positions its eponymous platform as a data lake engine that uses data virtualization and other techniques to provide speedy access to massive data sets.
Dremio’s platform is based on Apache Arrow, the open-source in-memory columnar data technology it codeveloped. The company combines this with several other open-source architectural components, such as the Gandiva execution kernel, the Apache Parquet columnar data storage format, and the Apache Arrow Flight data query response framework. Dremio layers these open-source components with its own proprietary technologies for query acceleration (which the company calls Data
Reflections); cloud caching (Columnar Cloud Cache in Dremio parlance); and data concurrency (or Predictive Pipelining as Dremio calls it). The combined open-source and proprietary components create a compelling architecture for fast in-place query access to vast data sets residing primarily on data lakes.
Dremio also offers a self-service business-centric semantic layer (what the company calls a virtual data set) over disparate data sources, with indexing, searching, and data lineage support also present. The platform supports connectivity to several major relational databases, such as Oracle, SQL Server, Postgres, MongoDB, and Amazon Redshift, among others. Given Dremio’s positioning as a data lake engine, support for connections to major distributed file systems is no surprise, with Azure Data Lake Storage, Amazon S3, and HDFS all being supported. Client tools can connect through REST, ODBC, JDBC, and Arrow Flight.
Multiple deployment options are offered, including on-premises Linux and UNIX environments; Hadoop installations; the native Kubernetes services of AWS and Azure (EKS and AKS, respectively); as
infrastructure-as-a service components in AWS and Azure through their native resource management tools (CloudFormation and ARM); and as a fully automated deployment and operationalized offering through Dremio AWS Edition.
Dremio offers a compelling blend of open-source innovation and proprietary enhancements that will primarily appeal to technically strong organizations looking to accelerate queries over large data sets.
The presence of a semantic layer, well-thought-out architecture, and extensive deployment options should add to the appeal of the platform.
Strengths: Well-designed open-source and company-developed architecture combination. Promises high-performance characteristics over large data sets. Wide array of deployment options.
Challenges: Potential open-source usage hesitation among some traditional organizations. Platform experience could be enhanced for non-technical users.
SAS Federation Server
Founded in 1976 and headquartered in Cary, NC, SAS is the elder statesman of vendors examined in this report. Its main data virtualization platform, SAS Federation Server, has been evolving for more than 20 years and has become one of the most well-known platforms on the market, especially among organizations that are already using other parts of the SAS platform portfolio.
The SAS Federation Server offers a virtualized data layer that helps integrate data from a variety of data sources, including Oracle, SQL Server, DB2, Hadoop, SAP HANA, Teradata, Greenplum, and IBM Netezza, through the use of federated data source names. Materialized views that combine data from these various sources can be created for improved performance, and in-memory caching capabilities are also available.
Integration with the vast SAS ecosystem is, as expected, a strong asset, with notable administrative tools and connections to the overall SAS data management and analytics solution set. JDBC and ODBC connections are supported, and a REST interface for data access and querying (called
“federated data as a service” by the company) is another data access option. Good data governance tools focused on data security and auditing are present, and several data quality functions round out the offering. Deployment is offered through the usual SAS avenues, including on-premises, in the public cloud, and as a service in SAS Cloud.
The SAS Federation Server offers a good solution for organizations already invested in the vast SAS ecosystem and, as such, should continue to see adoption. Appeal to organizations not skilled in or invested with SAS is an area the company would probably need to invest in to compete with the other solutions in the marketplace. Adoption of a business-centric semantic layer and creation of additional connections outside the SAS environment would also help it become an even more competitive platform.
Strengths: Excellent integration within the SAS ecosystem. Good support for federating data from major relational data sources.
Challenges: Tight integration within the SAS portfolio makes adoption a more difficult process for organizations not already heavily invested in the ecosystem. Lack of a semantic business layer is also problematic.
TIBCO Data Virtualization
Founded in 1997 in Palo Alto, CA, TIBCO is a well-known infrastructure software, integration, data management, and analytics provider. Its Data Virtualization platform, acquired from Cisco in 2017 and iterated upon since, is a well-rounded offering in the data virtualization space that is a fit for many organizations.
The TIBCO Data Virtualization platform enables the unification of different data sources into a single logical layer without data copying. Built as Java-based middleware, the platform offers distinct development, execution, and management environments that contain a variety of interconnected modules. These include a business directory for self-service data search, sharing, and metadata
support; a discovery module that allows the creation of data models and the discovery of relationships among the different data sources; a graphical modeling environment that supports visually creating transformations and using SQL and Java, among other languages; and several management,
optimization, and performance-enhancement modules, along with the usual array of execution engine components. Several data transformations are built-in, and a wide variety of data source adapters, along with the capability to create new ones, are present.
Other notable features of the platform include access to the business directory through a REST API; version control capabilities; a metadata repository; cache management; and a massively parallel processing engine. Native connectivity to data sources is strong, with ODBC and JDBC connectors present, along with more than 60 connectors for data sources such as Hadoop, Amazon DynamoDB, Amazon Redshift, Cassandra, and MongoDB; and applications from SAP, Oracle, Microsoft Dynamics, and Google Analytics. In all, TIBCO says it provides more than 300 connectors, more than 200 of which come from a partnership with CData.
Data can be consumed through ODBC, JDBC, ADO.NET, SOAP, and OData, among other protocols. Deployment on-premises on Windows, Linux, and UNIX, and on the major cloud platforms is
supported. Data lineage and robust management tools round out a strong set of features.
TIBCO Data Virtualization is a wide-ranging platform for data integration and virtualization and is a strong fit for many data virtualization use cases. Organizations of most sizes should keep the product in mind while researching solutions in this space.
Strengths: Feature set covers a wide swath of data virtualization use cases. Good visual design and management tools.
Challenges: Coherency of platform could be improved. Its collection of features, while impressively broad, could be more seamless. Business-centric semantic layer not at the forefront of the platform’s focus.
5.
Analysts’ Take
In this report, we build on the exploration of data virtualization technology presented in the recent GigaOm report, “Key Criteria for Evaluation Data Virtualization Platforms.” We leverage this earlier assessment of data virtualization product features and decision criteria to inform a detailed analysis of the vendors and solutions presented in this radar report. After our analysis across these reports, we conclude the following:
• Data virtualization technology can address organizations’ data needs while also managing growing datasets and the rapid proliferation of both data sources and access methods. Unifying these aspects is a high priority for organizations, which are now turning to data virtualization for a solution.
• Having a semantic data layer, ideally with business-centricity at its core, is extremely helpful, as it can drive adoption beyond the technical user base.
• Our exploration of the sector shows that there are multiple, excellent vendor platforms to choose from. While this may make selecting a platform more difficult, we consider this a good challenge to have.
• What these platforms have achieved is superb, providing capabilities that were practically
unthinkable just a few years ago: presenting a unified view from a full field of data sources in the enterprise sphere, allowing queries to run in-place across virtualized data sets, and presenting the results with impressive speed for consumption by a plethora of consumers. Organizations that ignore these capabilities are certain to be at a competitive disadvantage.
• Ease of use matters, even for deeply technical markets such as these. Doubly so when
organizations aim to bring non-technical data consumers into the fold, create a data-driven culture, and drive wide platform adoption.
Selection of an appropriate platform must be influenced by an organization’s preferred architectural philosophy and approach to the technology. Unlike other aspects of the data management stack, there is no universally accepted definition of how data virtualization technology and associated platforms should ultimately be implemented. Organizations should clearly define what they aim to achieve with their desired platform, perform deep architectural reviews, and choose platforms that offer the best blend of features within an implementation framework that feels most appropriate to them.
This report should help organizations evaluate some of the most popular approaches, and define both the current landscape and future direction of the market over the next few years.
6.
About Andrew Brust
Andrew BrustAndrew has held developer, CTO, analyst, research director and market strategist positions at organizations ranging from the City of New York and Cap Gemini to Gigaom and
Datameer. He has worked with small, medium and Fortune 1000 clients in numerous industries and with software companies ranging from small ISVs to large clients like Microsoft. Andrew’s resulting understanding of technology, and the way customers use it, makes his market and product analyses relevant, credible and empathetic.
Andrew has tracked the Big Data and Analytics industry since its inception, as Gigaom’s Research Director and ZDNet’s lead blogger for Big Data and Analytics. Andrew co-chairs Visual Studio Live!, one of the nation’s longest running developer conferences. As a longtime technical author and speaker in the database field, Andrew understands today’s market in the context of its longtime Enterprise underpinnings.
7.
About Yiannis Antoniou
Yiannis AntoniouYiannis Antoniou is a technologist with over 20 years of global experience in the financial industry. He has served as a CTO in the asset management industry, as a management consultant in the banking & insurance industries and as a technical architect, project manager, development and infrastructure manager in major financial firms in the US and Europe. Major organizations in his tenure include Goldman Sachs, JPMorgan, AIG, Pacific Global Advisors, EY and BNY Mellon, holding various technical management positions in the Asset Management, Investment Management, Enterprise Risk Management, Strategic Planning, Investment Banking, Insurance, Innovation and Digital Transformation areas.
Yiannis delivers technology expertise in data & analytics, cloud, application development, technology architecture, technology operations, technology infrastructure, AI and ML, Blockchain, and DevOps in enterprise and startup settings. He combines ‘go to market’ expertise with practical application of agile product, project, program and portfolio management processes and has managed and implemented more than 200 programs, projects and engagements with cumulative budgets of over $500 million. Yiannis is a graduate of the National Technical University of Athens, Greece where he worked in a variety of European Union research projects in the fields of energy and financial modeling, built applications, taught database systems and design and published research papers in peer-reviewed journals.
8.
About GigaOm
GigaOm provides technical, operational, and business advice for IT’s strategic digital enterprise and business initiatives. Enterprise business leaders, CIOs, and technology organizations partner with GigaOm for practical, actionable, strategic, and visionary advice for modernizing and transforming their business. GigaOm’s advice empowers enterprises to successfully compete in an increasingly
complicated business atmosphere that requires a solid understanding of constantly changing customer demands.
GigaOm works directly with enterprises both inside and outside of the IT organization to apply proven research and methodologies designed to avoid pitfalls and roadblocks while balancing risk and innovation. Research methodologies include but are not limited to adoption and benchmarking surveys, use cases, interviews, ROI/TCO, market landscapes, strategic trends, and technical
benchmarks. Our analysts possess 20+ years of experience advising a spectrum of clients from early adopters to mainstream enterprises.
GigaOm’s perspective is that of the unbiased enterprise practitioner. Through this perspective, GigaOm connects with engaged and loyal subscribers on a deep and meaningful level.
9.
Copyright
©Knowingly, Inc.2020 "GigaOm Radar for Data Virtualization" is a trademark ofKnowingly, Inc.. For permission to reproduce this report, please [email protected].
