![]() ![]() There is some confusion over Delta Lake as there is both an open source version and a more robust custom version that Databricks commercializes with their offering. Delta Lakeĭelta Lake provides a table format layer over your data lake that gives you a database-like schema view and style ACID transactions through logs that are associated with each Delta table. MLflow can be used as a regular logging system in data science development. That information is then available to compare previous runs and to execute a previous test. MLflow tracks machine learning experiments by logging parameters, metrics, versions of data and code, and any modeling artifacts from a training run. It can be compared to tools such as Amazon Sagemaker. AutoML and MLflowĭatabricks AutoML is a service that enables you to build machine learning models in a low-code environment. Compute resources are allocated via Runtimes (which we cover below). A workspace contains all the assets and libraries used in your Databricks environment specific jobs are run through notebooks (similar to tools such as Jupyter or Google Colab). Workspaces and Notebooksĭevelopment in Databricks is organized in Workspaces and Notebooks. This means a solid understanding of Spark is essential for any work done with Databricks. Databricks at its heart is a managed service for Spark, which is a core component of the Databricks ecosystem. It enables you to query data stored across hundreds of machines. Source: Microsoft What are the Key Features and Components of Databricks?ĭatabricks is comprised of several component technologies that we will describe briefly: SparkĪpache Spark is an open-source cluster computing system for fast and flexible large-scale distributed data processing. An example of a Databricks reference architecture on Azure. Their products purport to facilitate data engineering, data science, and machine learning – but as we shall see, they are not necessarily equally well-suited for all use cases. It has since grown to be a major contributor to data lake technology across all the major cloud platforms. What is Databricks? A Quick Overviewĭatabricks is a U.S.-based company, founded by the team behind Apache Spark. In other cases, Upsolver can and is used alongside Databricks. We hope this will help you make a more informed decision when evaluating Databricks or other data technologies.įull disclosure: Upsolver competes with Databricks for certain use cases around large-scale data processing, as well as data lakehouse architectures. Towards this end, this article provides a brief overview of Databricks – its components, use cases, and some of the strengths and weaknesses we’ve learned based on online reviews and conversations with Databricks users. No single tool will solve every data engineering challenge you’re facing, and it’s important to understand where various software is more or less useful. However, as with any technology, making informed decisions requires cutting through the noise. With a staggering $3.5B in private funding, a heated dispute with Snowflake over performance (see Snowflake vs Databricks), and a large and established customer base, Databricks is certainly a force to be reckoned with in today’s cloud data ecosystem. If you’re looking to build a modern data stack, you’ve probably heard of Databricks. ![]()
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