Watch full webinar here: https://bit.ly/3A7bSYo The data landscape has become more complex with new use cases like IoT, Streaming and Edge analytics. To better manage enterprise data distributed across data lakes, data warehouses, and many other systems residing on-premises and in the cloud, enterprises are turning to various data management technologies like query accelerators, data virtualization, and data fabrics. Each address specific needs of an organization, but which is the right solution to help you meet your business objectives? Join Denodo for a webinar featuring guest speaker, Forrester VP and Principal Analyst, Noel Yuhanna, to understand the differences between query accelerator, data virtualization, and data fabric and when to use which technology. In this webinar, you will learn the following: - The latest trends in the data management landscape - Differences between three popular data management technologies - How to graduate from one technology to another - Real customer examples of how they use these technologies

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
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