Analytics

We're buzzing with excitement as we unveil two killer features that are bound to improve your data operations:‍

At three full days and nearly 4,000 registered attendees, Gartner Data & Analytics Summit 2023 in London was the largest data and analytics summit in EMEA to date. That much interest in data and analytics thrills us, of course — but it was also exciting because it was one of our first major events following the announcement that Talend has been acquired by Qlik. There was a lot to talk about!

One of the world’s largest logistics companies leverages automation and AI to empower every individual data engineer with self-service capability to optimize their jobs for performance and cost. The company was able to cut its cloud data costs by 70% in six months—and keep them down with automated 360° cost visibility, prescriptive guidance, and guardrails for its 3,000 data engineers across the globe.

AI Chatbots are being talked about everywhere. Product managers rely heavily on collaborations with stakeholders to achieve success with their strategic objectives. These stakeholders include developers, sales teams, marketing teams, executives, and other key players. Yet these stakeholders alone do not guarantee success for your customers and clients. As a product manager, you need to use all the tools at your disposal to add value to their strategies and goals.

Self-service analytics has been a leading priority in the business intelligence (BI) space for years and is likely here to stay. With data-driven culture on the rise, analytics is no longer just for IT teams and data scientists. Self-service business intelligence tools make it possible for personnel across functions to perform analytics-related tasks themselves, dramatically reducing time to insight.

Discover how to optimize your procurement process with our guide to 21 crucial KPIs. Drive efficiency, foster relationships, and fuel sustainable growth.

Historically, connecting multiple data sources to a single destination required extensive experience as a computer programmer or data scientist. Today’s no-code data pipelines have changed that perspective. Now, practically anyone – even those without any coding experience – can use no-code pipelines to streamline data processing without damaging data quality. You will, however, need the right ETL and ELT tools to manage real-time data flows.

Are you a data scientist or data engineer looking to improve your data operations and workflows? Look no further than Snowpark! Our team at Keboola has conducted comprehensive benchmark tests pitting Snowpark against Spark, a traditional data processing engine. Our findings? Snowpark outperforms Spark in both speed and cost for most data engineering and machine learning tasks. ‍ Inside, you’ll find: ‍ Unlock the full potential of your data.