The Evolution of Data Stack: From Query Accelerators to Data Fabrics
0 likes12 views
The document discusses the evolution of data management technologies, focusing on challenges faced in 2023, such as data silos and the need for real-time data. It explains the roles of query accelerators and data virtualization, highlighting their capabilities and differences, while positioning them as part of a broader data fabric platform. The discussion concludes with predictions about the future of data management technologies, emphasizing a trend towards integrated solutions and enhanced data intelligence.
The Evolution of Data Stack: From Query Accelerators to Data Fabrics
- 1. The Evolution of Data Stack: From Query Accelerators to Data Fabrics A Discussion with Forrester’s Noel Yuhanna Moderated by: Ravi Shankar Senior Vice President and Chief Marketing Officer Denodo
- 2. SPEAKERS Guest Speaker - Noel Yuhanna Vice President and Principal Analyst Forrester Saptarshi Sengupta Sr. Director of Product Marketing, Denodo
- 3. 3 1. What are some of the most important data management challenges in 2023? What are/were the biggest challenges in executing your vision for data, data management, data science, and analytics? Base: 3627 Data and analytics decision-makers Source: Forrester's Data And Analytics Survey, 2022 24% 24% 21% 20% 20% 20% 19% 19% 18% 18% 17% 0% 6% 12% 18% 24% 30% Maturity of technology around security Maturity of technology around data management Inability to process big data and act on it at the speeds… Organizational business issues with data stewardship… Lack of business competency to deal with data that is… Accessibility, availability, and/or readiness of data to use Lack of collaboration between teams Lack of executive support to develop big data capabilities Lack of foundational investments Understanding the data Lack of technology skills ©Forrester Research, Inc. All rights reserved.
- 4. 4 1. What are some of the most important data management challenges in 2023? ▪ Lower cost ▪ Data silos – hybrid, multi-cloud – issues ▪ Lack of trusted, integrated data ▪ Need for real-time data for apps, insights ▪ Strong compliance requirements ▪ Improved automation to deal with operational efficiencies ▪ Lack of agility… ©Forrester Research, Inc. All rights reserved.
- 5. 5 2. Can you please highlight some of the technologies that alleviate these data management challenges? ©Forrester Research, Inc. All rights reserved.
- 6. 6 3. Can you explain Query Accelerator and its capabilities? ▪ Querying data stored in data lakes, object stores and complex data warehouses. ▪ Fetch only selected data from distributed data ▪ Help businesses accelerate analytics and data search through simplified queries… ▪ Often used by data engineers, data analysts, developers… ©Forrester Research, Inc. All rights reserved.
- 7. 7 3a. Can you talk about how customers use Denodo for query acceleration? ▪ Dynamic Query Optimization ▪ Smart Query Acceleration ▪ Massive Parallel Processing (MPP) Nearly every customer uses Denodo for query acceleration
- 8. 8 4. What are the sweet spot use cases of Query Accelerator and where do these solutions hit a ceiling? ▪ Sweet spot – querying data from data lakes, object stores quickly using SQL, procedural language ▪ Help accelerate development of discovering new data sets and patterns, knowing your data ▪ Improves productivity of developers, data engineers.. ▪ Ceiling – lack of true data integration across multiple data sources, lack of data transformation, lack of data governance/security, lack of data quality... ©Forrester Research, Inc. All rights reserved.
- 9. 9 5. Can you please explain the difference between Query Accelerator and Data Virtualization? ▪ Query accelerator focuses on accessing data from data lakes, object stores quickly ▪ DV focuses on more than query accelerator, it offers data integration (federating across sources), security, transformation, caching, metadata management/catalog, access to data using ODBC/JDBC, SQL… it’s a platform vs. only a query accelerator… ©Forrester Research, Inc. All rights reserved.
- 10. 10 5a. Can you talk about how customers use Denodo for data virtualization? ▪ Universal Semantic Layer ▪ Consistent, centralized data security and governance ▪ Data services using REST, OData, and GraphQL Data Virtualization is part of the Denodo DNA
- 11. 11 6. Is it fair to assume that technologies like Query Accelerator and Data Virtualization are part of a Data Fabric platform? ©Forrester Research, Inc. All rights reserved.
- 12. 12 7. What capabilities does a data fabric offer beyond data virtualization? ▪ Data fabric goes beyond DV to include, data governance, data quality, data modeling, AI/ML, data intelligence, API interface, focusing on broader use cases such as customer 360, customer intelligence, risk analytics, IoT analytics… ©Forrester Research, Inc. All rights reserved.
- 13. 13 7a. Customer who evolved Denodo to a full-fledged data fabric platform? ▪ Augmented Data Catalog ▪ Active Metadata ▪ AI/ML-driven Recommendation Engine
- 14. 14 8. How do you see these different data management technologies evolve over the next 2-3 years? Which ones do you think will prevail and why? ▪ Most organization will look at an end-to-end data management solution – rather than trying to integrate multiple products... Hence, we will see an increased adoption of data fabric – for more use cases… ▪ We are heading more towards data intelligence, where the need to access data that’s more semantically driven rather than just accessing the data… ©Forrester Research, Inc. All rights reserved.
- 15. 15 Next Steps Access Denodo Platform in the Cloud. Start your Free Trial today! GET STARTED TODAY www.denodo.com/free-trials Logical Data Fabric A Technical Whitepaper DOWNLOAD WHITEPAPER
- 16. Thanks! www.denodo.co m info@denodo.com © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.