Modern organizations generate and collect vast amounts of log data each day from an ever-increasing number of sources that includes IT infrastructure, networking devices, applications, cloud services, security tools, and more. This data is essential for powering use cases from security operations and threat hunting to application performance monitoring, but tapping into the full potential of log data can be challenging for organizations without the right tools and capabilities.

Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.

Many enterprises face significant challenges when it comes to building data pipelines in AWS, particularly around data ingestion. As data from diverse sources continues to grow exponentially, managing and processing it efficiently in AWS is critical. Without these capabilities, it’s harder to analyze and get any meaning from your data.

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.

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.

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.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.

Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities. Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives.