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Sep 4, 2024 | 
dbta.com | Joe McKendrick |Sumit Pal |Audra Simons
Volume 38 | Number 5 AUGUST/SEPTEMBER 2024 CONTENTS FEATURES RESEARCH@DBTA 2 SURVEY: WE’RE STILL ONLY BEGINNING TO BUILD DATA-DRIVEN ORGANIZATIONS BY JOE MCKENDRICK FEATURE STORY 5 THE INS AND OUTS OF BUILDING A MICROSERVICES ARCHITECTURE BY JOE MCKENDRICK READERS’ CHOICE AWARDS 2024 > SPECIAL SECTION 9 INTRODUCTION 10 DATABASE TRENDS AND APPLICATIONS READERS’ CHOICE AWARDS 2024 DEPARTMENTS DBTA LIVE 14 EMPOWERING AI INITIATIVES WITH MODERN DATABASE STRATEGIES AND TECH 15 EXPLORING DATA...
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Feb 14, 2024 | 
tdwi.org | Sumit Pal
In the last part of our series, we examine how data mesh enhances performance and can help your organizations and data teams work more effectively.
In the first part of our series we introduced the basics of data mesh. In part 2 we explored best practices for adopting the technology. In this, the last part of our series, we examine several of the organizational benefits data mesh offers.
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Feb 13, 2024 | 
tdwi.org | Sumit Pal
Best practices for successfully adopting the data mesh paradigm in your organization.
Part one of this three-part series discussed the concept of data mesh and explored what it is and why an organization should care. Here, part two provides best practices for data mesh, including practical guidance, challenges, and limitations.
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Feb 12, 2024 | 
tdwi.org | Sumit Pal
We explain the basics of the data mesh and whether it’s a good fit for your enterprise.
Although the enterprise data landscape is littered with new data technology and offerings, the most pressing problem data teams face today isn’t a lack of technology or skills; it’s not knowing how to create a modern data experience. Data teams are struggling to tame the chaos and control the entropy of the data ecosystem and are evaluating modern approaches such as the data mesh to manage the confusion.
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Nov 29, 2023 | 
confluent.io | Sumit Pal
This blog post is part three of a three-part series on how businesses can drive value using real-time data streaming and processing technologies. Check out parts one and two for more insights.
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Oct 30, 2023 | 
clouddatainsights.com | Sumit Pal
Structured and unstructured streaming data are ubiquitous – we just don’t realize it. From data continuously streaming from phones to sensors gathering and streaming data, CCTVs everywhere, streaming data is zooming by all the time. Streaming technologies are being leveraged by organizations across different verticals, like supply chain, eCommerce, cybersecurity, and many more, to harness this flowing data and derive the right insights at the right time and place.
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Oct 26, 2023 | 
rtinsights.com | Sumit Pal
With stream processing flipping the data processing paradigm from store then process to process then store, the critical capabilities to evaluate when building streaming applications are vastly different from those for batch. Here we discuss the available choices for building streaming solutions and a high-level guidance for selecting them. Structured and unstructured streaming data are ubiquitous – we just don’t realize it.
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Oct 12, 2023 | 
confluent.io | Sumit Pal
Oct 12, 2023Read Time: 7 minOct 12, 2023Read Time: 7 minWelcome to the new stream processing paradigm!This blog post is part two of a three-part series on how businesses can drive value using real-time data streaming and processing technologies. Stay tuned for part three, coming soon!Stream processing flips the data processing paradigm – from store then process (as in batch process) to process then store if needed.
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Oct 11, 2023 | 
bmc.com | Sumit Pal
Introduction Organizations are drowning in data and thirsty for knowledge, wisdom, and insights. Data engineering teams in aspiring data-driven organizations are overwhelmed with fast-changing technology and organizational complexities as they look to move from proof of concept (POC) to proof of value (POV) and establish a sustainable operating model with continuous improvement.
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Oct 11, 2023 | 
s7280.pcdn.co | Sumit Pal
Introduction Organizations are drowning in data and thirsty for knowledge, wisdom, and insights. Data engineering teams in aspiring data-driven organizations are overwhelmed with fast-changing technology and organizational complexities as they look to move from proof of concept (POC) to proof of value (POV) and establish a sustainable operating model with continuous improvement.
