Systems | Development | Analytics | API | Testing

September 2024

5 Ways to Approach Data Analytics Optimization for Your Data Lake

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.

Data AI Summit | Expanding Log Analytics and Threat Hunting Natively in Databricks

ChaosSearch + Databricks Deliver on the best of Databricks (open Spark-based data lakehouse) and ELK (efficient search, flexible live ingestion, API/UI) via ChaosSearch on Databricks. Log analytics for observability / security with unlimited retention at a fraction of the cost now with Databricks’ AI/ML. Watch as ChaosSearch CEO, Ed Walsh, shares the power of ChaosSearch in your Databricks environment.

5 Challenges Querying Data in Databricks + How to Overcome Them

Databricks is lighting the way for organizations to thrive in an increasingly AI-driven world. The Databricks Platform is built on lakehouse architecture, empowering organizations to break down existing data silos, store enterprise data in a single centralized repository with unified data governance powered by Unity Catalog, and make the data available to a variety of user groups to support diverse analytics use cases.

Databricks Data Lakehouse Versus a Data Warehouse: What's the Difference?

Businesses today rely heavily on data to inform decisions, predict trends, and optimize operations. However, more data volume and complexity has led to growing pressure to find scalable, cost-effective solutions for data storage while staying within IT budgets. Companies want to handle both structured and unstructured data efficiently, while supporting advanced data analysis and machine learning use cases.