Building Next-Gen Data Pipelines with Databricks Delta


Building performant ETL pipelines to address analytics requirements is hard as data volumes and variety grow at an explosive pace. With existing technologies, data engineers are challenged to deliver data pipelines to support the real-time insight business owners demand from their analytics. Databricks Delta is the next generation of evolution in big data processing from Databricks, the company founded by the original creators of Apache Spark.
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How can KPMG use Data and Analytics to assist your company?


Data Analytics Testautomation Concept

In this 50-minute overview and product demo, Founder and CTO, Thomas Bolt, will show you how you get a strategic and automated concept for quality assurance in your data-driven development project. You’ll learn why hundreds of data analytics projects fail with too less or inadequate testing and how to implement a strong testing concept easily by using BiG EVAL's test automation software for data-driven projects.
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How does AXA Belgium Replicate its operational data into its Data Lake?

To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.
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Accelerating Project Delivery Fast & Data Compliant

Watch our webinar fast data delivery demo for a walkthrough, showing you how to leverage Accelario to increase DevOps velocity, while achieving data compliance and reducing storage costs.
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Empowering Data Scientists with MLOps

Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.
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