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Access Control

Apply dynamic row level security, column-level security, and even cell-level protection on every Spark job and query across SQL Analytics and Data Science workspaces.

Data Access

Understand who accessed what data, when, and for what purpose.

Data Catalog

Enable democratized, subscription-level data access with always-on security and access control.

“We needed to expedite our data processing, while also finding a way to dynamically anonymize sensitive information for reporting. We therefore required a solution that could help us enforce data access roles, permissions and policies beyond the standard resource- or table-based control levels.”

Halim Abbas



Fine-Grained Access Control

Securing analytics data is often a manual effort requiring data copies, manually stripping out or anonymizing sensitive information and provisioning role-based access to specific tables. With Immuta, you can now dynamically apply row-level security, column-level security and data masking, and cell-level data protection to secure sensitive data without copying it, manually preparing it or managing role-based access. Immuta’s modern, attribute-based access controls (ABAC) are dynamically enforced on Spark jobs and queries across SQL Analytics and Data Science workspaces, providing fine-grained security over sensitive analytics data while vastly improving data engineers’ productivity.

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Data Access Auditing

Data engineers today must ensure all analytics data and data use is compliant with a complex, growing set of regulatory and business rules. Immuta and Databricks have greatly simplified this process by ensuring all Spark jobs are automatically logged in Immuta. Legal teams can view detailed audit logs and reports that show data consumers’ access levels, intended purposes and query history — all in plain English.

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Self-Service Data Catalog

While the market is filled with offerings that claim to provide a unified data catalog, most do not enable true self-service, subscription-based access to live data due to the inherent security risks. Immuta’s active data catalog is built on a strong security foundation with always-on governance and access control. As a result, Databricks analysts and data scientists can use Immuta to search, explore and subscribe to data sources. Immuta is always working in the background to ensure local and global data policies are applied dynamically to Spark workloads and queries across SQL Analytics and Data Science workspaces.

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Immuta’s Automated Data Access Control platform – now natively integrated with Databricks – enables organizations to perform data science faster and more securely by dynamically protecting and anonymizing data. Databricks customers can enforce fine-grained data access controls directly within Databricks’ Apache Spark™ unified analytics engine for Big Data and machine learning, and Delta Lake, its open-source storage layer for Big Data workloads.

Results for Data Teams

40% Increase in data engineering productivity when managing
sensitive data.

25%-90% Increase in permitted use cases for cloud analytics by safely unlocking sensitive data.

Reduce to seconds what can be a months-long process to provide self-service data access.


Sign up for a free Databricks trial

To help you get to know Databricks, you can try it out for free. You can choose between Databricks Platform Free Trial and Community Edition subscriptions. Both options give you free Databricks units (DBUs), units of Apache Spark processing capability per hour based on VM instance type.

The Databricks Platform Free Trial gives you access to the full Databricks product. It is more full-featured and flexible than Community Edition, but Databricks uses compute and S3 storage resources in your AWS account. During the sign-up process you grant Databricks privileges to access your AWS account and an S3 bucket. When you register for the trial, you can request credit for free AWS resources. When your free trial ends you receive an email informing you that you are automatically enrolled in the plan that you selected when you signed up for the trial. You can upgrade to a higher tier plan at any time after your trial ends.

Databricks Community Edition is fully resourced; you are not required to supply any compute or storage resources. However, several features available in the Databricks Platform Free Trial, such as the REST API, are not available in Databricks Community Edition. For details, see Databricks Community Edition FAQ.

You can cancel either subscription at any time in the account console.

This article describes how to sign up for a subscription.

Launch the signup wizard and select a subscription type

  1. Click Try Databricks here or at the top of this page.

  2. Enter your name, company, email, and title, and click GET STARTED FOR FREE.

  3. Select the free program you want:

    • For Community Edition, click the GET STARTED button.
    • For a 14-day free trial, select the cloud provider you want use: Azure, AWS, or Google Cloud. The Databricks trial is free, but you must have a cloud account, and Databricks uses compute and storage resources in your cloud account. Each cloud provider has programs for free credits during your free trial.
    Try Databricks
  4. Look for an email asking you to verify your email address.

  5. Follow the instructions for your selection: Databricks Platform Free Trial or Community Edition.

Databricks Platform Free Trial

After you’ve signed up for a free trial, you’ll see a page announcing that an email has been sent to the address you provided. To learn what to do next, follow the instructions in Set up your Databricks account and deploy a workspace.


The Databricks trial is free, but you must have an AWS account, and Databricks uses compute and S3 storage resources in your AWS account. If you want to request free AWS credits, go to the Proof of Concept Credit Available page. Fill out and submit the PoC credit application and wait until you are contacted by Databricks before you continue with the trial registration process.

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NOTE: Koalas supports Apache Spark 3.1 and below as it will be officially included to PySpark in the upcoming Apache Spark 3.2. This repository is now in maintenance mode. For Apache Spark 3.2 and above, please use PySpark directly.

pandas API on Apache Spark
Explore Koalas docs »

Live notebook · Issues · Mailing list
Help Thirsty Koalas Devastated by Recent Fires

The Koalas project makes data scientists more productive when interacting with big data, by implementing the pandas DataFrame API on top of Apache Spark.

pandas is the de facto standard (single-node) DataFrame implementation in Python, while Spark is the de facto standard for big data processing. With this package, you can:

  • Be immediately productive with Spark, with no learning curve, if you are already familiar with pandas.
  • Have a single codebase that works both with pandas (tests, smaller datasets) and with Spark (distributed datasets).

We would love to have you try it and give us feedback, through our mailing lists or GitHub issues.

Try the Koalas 10 minutes tutorial on a live Jupyter notebook here. The initial launch can take up to several minutes.

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Getting Started

Koalas can be installed in many ways such as Conda and pip.

# Conda conda install koalas -c conda-forge

See Installation for more details.

For Databricks Runtime, Koalas is pre-installed in Databricks Runtime 7.1 and above. Try Databricks Community Edition for free. You can also follow these steps to manually install a library on Databricks.

Lastly, if your PyArrow version is 0.15+ and your PySpark version is lower than 3.0, it is best for you to set environment variable to manually. Koalas will try its best to set it for you but it is impossible to set it if there is a Spark context already launched.

Now you can turn a pandas DataFrame into a Koalas DataFrame that is API-compliant with the former:

importdatabricks.koalasasksimportpandasaspdpdf=pd.DataFrame({'x':range(3), 'y':['a','b','b'], 'z':['a','b','b']}) # Create a Koalas DataFrame from pandas DataFramedf=ks.from_pandas(pdf) # Rename the columnsdf.columns= ['x', 'y', 'z1'] # Do some operations in place:df['x2'] =df.x*df.x

For more details, see Getting Started and Dependencies in the official documentation.

Contributing Guide

See Contributing Guide and Design Principles in the official documentation.


See FAQ in the official documentation.

Best Practices

See Best Practices in the official documentation.

Koalas Talks and Blogs

See Koalas Talks and Blogs in the official documentation.

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