In an era where data integration forms the backbone of modern digital ecosystems, effectively mastering REST APIs is crucial for any data engineer to ensure efficiency and reliable data transfers. Our 5 key takeaways from the article on REST API best practices for data integration are.

Are you a ThoughtSpot enthusiast? Maybe you built a liveboard that saved your department hours each work week, or perhaps you figured out a unique way to gamify adoption across your team. You put in the hard work, now it’s time to show it off. ThoughtSpot User Groups were designed to help users connect—a place where you can share stories and get new ideas to empower your organization with data.

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

Today’s businesses generate more data than ever before. You need that data to be consistent and reliably accessible across disparate systems. But how? Here are five key takeaways concerning data replication tools: With multiple databases, applications, platforms, and other tools constantly generating data, it can be a challenge to maintain consistency and accessibility. Even the slightest error can mean unhappy customers, unreliable reports, failed analytics, and compliance issues.

If 2023 was the year we woke up to how generative AI would change our world, 2024 is the year we realize the change. The real-time AI-driven enterprise may not be pixel-perfect yet, but we’re well on the way. Gen AI has a knock-on effect on all the trends and challenges we will see in 2024. Here’s our take.

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

In the final month of 2023, Snowflake released features around Snowflake Cortex functions, Snowpark ML, cost management and more. Read on to learn more about everything we announced in December.

Today, at Qlik, we continue our exciting journey by revealing a completely new brand and website. This transformation goes beyond aesthetics, symbolizing our dynamic evolution at the forefront of data integration, analytics, and AI. Our new identity encapsulates innovation, clarity, and agility. In the current landscape, CEOs face the challenge of harnessing AI effectively—a realm where Qlik's unique agility and independence provide unparalleled advantage.

This blog was written based on a collaborative webinar conducted by Hevo Data and Danu Consulting- “Data Bytes and Insights: Building a Modern Data Stack from the Ground Up”, furthering Hevo’s partnership with Danu consulting. The webinar explored how to build a robust modern data stack that will act as a foundation towards more advanced data science applications like AI and ML. If you are interested in knowing more, visit our YouTube channel now!