What are the top three mega-trends for data leaders this year (and beyond)? In this episode, we tackle cloud data platforms, the five sub-disciplines of observability, and real-time machine learning. During our conversation with Kevin Petrie, Vice President of Research at Eckerson Group, we’ll tackle: Ready to learn more about managing the modern-day mountains of data at our fingertips? Let’s dive in.

Ever since there was a need to both store and access information, there has been both physical and logical means to achieve it. Everything from stone tablets to paper, to a prolifera of technology in the digital age. As information became easier to create, databases were built to give it structure to simplify its access, accompanied by characteristics to improve performance and scale.

When providing the data to support marketing, it's important to frame and validate its quality based on whether it meets "the six C’s." In our conversation with Christopher Penn, Co-Founder and Chief Data Scientist at TrustInsights.ai, we discuss questions many in the industry are asking today.

The cloud data platform combines data warehouse and data lake capabilities to support the exploding world of analytics. Like a data warehouse, the cloud data platform structures, transforms, and queries data. Like a data lake, it classifies multi-structured data objects in an elastic object store. The cloud data platform provides an ideal launchpad for modern business intelligence (BI) projects that need fast, flexible access to lots of varied data. As you might expect, this is a tall order to fill.

Welcome to the second installment of our ChaosSearch for FinTech blog series, where we explore how financial technology (FinTech) companies can solve analytics challenges and drive business outcomes with ChaosSearch. In Part One of this series, we brought you an in-depth look at how FinTech companies could accelerate application development and streamline operations in the cloud by adopting ChaosSearch for log analytics at scale.

A universal human problem that we don’t often address is that historically, knowledge has been relatively siloed by language. But with advancements in AI, there are new opportunities to capture broader and deeper insights across the written and spoken word by breaking down global language and distance barriers. This was the topic of discussion on our most recent Data Legends podcast episode, featuring Amr Awadallah, founder and CEO at ZIR AI and former technology exec at Cloudera, Google, and Yahoo.

Last week, Gartner released their 2022 Gartner Market Guide for Analytics Query Accelerators (AQAs), which names ChaosSearch as a Representative Vendor. In this week’s blog, we share perspectives from both Gartner and ChaosSearch on the emerging AQA category that focuses on accelerating data lakes’ time to value. You’ll learn what AQAs are, their role in the enterprise tech stack, and how ChaosSearch differentiates itself from others in the segment.

Last week, ChaosSearch CTO & Head Scientist Thomas Hazel joined forces with Doug Henschen, VP & Principal Analyst of Constellation Research, to deliver a live webinar on “Choosing an Analytical Cloud Data Platform: Trends, Strategies, and Tech Considerations.” In this blog, we’re highlighting the deepest insights, the best advice, and the most actionable recommendations that emerged from the discussion.

ChaosSearch addresses critical pain points and overcomes core operational challenges for FinTech companies, allowing them to accelerate application development and streamline their operations in the cloud. The ChaosSearch data lake platform delivers search and relational analytics at scale directly in Amazon S3, with no data movement, no ETL process, and zero administrative overhead.

When CTO James Dixon coined the term “data lake” in 2011, he imagined a single storage repository where organizations could store both structured and unstructured data in their raw format until it was needed for analytics. But without the right storage technology, data governance, or analytical tools, the first data lakes quickly became “data swamps” - morasses of data with no organizational structure and no efficient way to access or extract meaningful insights.