Databricks is a cloud-based unified data analytics platform that provides a collaborative environment for big data and AI workloads. It integrates with Apache Spark to offer a managed platform where users can run Spark clusters, perform large-scale data processing, and build machine learning models.
Databricks simplifies the complexities of setting up Spark clusters by handling infrastructure, scaling, and performance optimization automatically.
To create a cluster in Databricks:
Once the cluster is created, you can run your notebooks or jobs on it.
Delta Lake is an open-source storage layer that provides ACID (Atomicity, Consistency, Isolation, Durability) transactions on top of existing data lakes. It helps Databricks users by offering:
Databricks provides a Job UI where you can track the status of jobs, view logs, and check the performance of your Spark jobs. You can also set up alerts to monitor failures and slow executions.
To speed up Spark jobs in Databricks:
groupBy
) which require shuffling..repartition()
 or .coalesce()
.Databricks supports Git integration for version control. You can link your notebooks to GitHub, GitLab, or Bitbucket.
Databricks provides security at multiple levels:
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including:
In Databricks, MLflow is integrated natively, and you can use it for experiment tracking and model management.
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Databricks supports structured streaming for real-time data processing.
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Partitioning helps optimize data processing by ensuring that data is split into manageable chunks. Proper partitioning reduces shuffling, speeds up queries, and improves overall performance.
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Databricks integrates seamlessly with cloud storage services like Azure Blob Storage, Amazon S3, and Google Cloud Storage. You can configure your clusters to mount cloud storage or access cloud data directly using paths.
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You can process data incrementally using structured streaming and Delta Lake.
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You can schedule tasks in Databricks using the Jobs UI. Jobs can be triggered on a schedule or triggered by another job, and they can run notebooks, Python scripts, or JAR files.
Example:
Time travel allows you to query historical versions of data in Delta Lake by using the versionAsOf
 or timestampAsOf
 options.
groupBy
, join
) whenever possible. Use partitioning and broadcasting.salting
 techniques to evenly distribute data.