Today, Data Analytics has become one of the most in-demand skills. This allows the organizations to carry out effective data analysis to derive insights from the data and make data-driven decisions.
Many new techniques for integrating data from Data Engineering platforms to BI (Business Intelligence) tools have emerged in this rapidly evolving field. One such technique is the Redash Databricks Integration.
Redash is a popular BI solution that provides users with a Collaborative Visualization and Dashboard platform.
Thousands of companies, like Cloudflare and SoundCloud, have embraced this product as it allows them to seamlessly run SQL queries and generate dashboards to communicate with decision-makers.
On the other hand, Databricks is a Cloud-based Data Engineering platform that is widely used by businesses to Process, Transform, and Explore large amounts of data. It can also be used to perform interactive analysis and create Machine Learning applications.
This article will guide you through the process of setting up Redash Databricks Integration using 4 simple steps. It will also provide you with a brief overview of Redash and Databricks with their key features and the benefits of setting up Redash Databricks Integration.
You will have a much easier time setting up your Redash Databricks Integration if you have gone through the following prerequisites:
- An active Redash account.
- An active Databricks account.
Introduction to Redash
Redash is one of the popular Collaborative Dashboarding and Visualization tools that allow users to interact with data regardless of their technical knowledge.
When compared to other Data Visualization platforms, Redash provides a plethora of robust integration functionalities. This feature makes it a favorite among organizations that have a variety of applications for managing their business processes.
You can also seamlessly integrate Redash with Data Warehouses, perform SQL queries to select subsets of data for visualizations, and share dashboards with ease.
Overall, Redash will help your organization adopt a data-driven mindset, which is critical in today’s cut-throat business world.
You’ll have all the information you need about your business at your fingertips with Redash Dashboards. Redash is quite popular among SQL users since it enables them to query their data.
Key Features of Redash
Redash provides a wide range of features that makes it unique from other BI tools. Some of the key features of Redash include:
- Query Editor: Query Editor is a powerful and unique feature in Redash Dashboard that allows you to write SQL and NoSQL queries for your data. It also offers an auto-query feature that allows you to perform a query without any manual intervention.
- Easy Collaboration: With a single click on a secret URL (Uniform Resource Locator), Redash Dashboards allows you to collaborate with your peers or clients. This collaboration feature will provide real-time information to all employees in your organization, thus, allowing them to make more informed decisions.
- Interactive Dashboards: Redash Dashboards include features such as Cohort Analysis, Chart Visualizations, Funnel Visualizations, Pivot Tables, Box Plots, Maps, Sunburst, Sankey, and more.
- Scheduled Refreshes: Redash Charts and Dashboards can also be automatically updated and shared with colleagues.
- REST API: Redash also offers REST API and it allows users to do everything that can be done with the UI.
- Updates and Alerts: Redash Dashboards allow you to automate your Dashboard data and receive notifications when the attention is needed. You may also schedule refreshes to update your charts and dashboards at regular intervals using the Automated Refresh feature.
- Native Support for Data Sources: Redash also comes with an extensible data source API that provides native support for most of the popular SQL and NoSQL databases among other platforms.
To know more about Redash, visit this link.
Introduction to Databricks
Databricks is a popular Cloud-based Data Engineering platform for handling and manipulating large amounts of data. It allows you to easily extract insights from your existing data while also assisting you in the development of AI (Artificial Intelligence) solutions.
It also offers Machine Learning libraries such as Tensorflow, Pytorch, and others for training and constructing Machine Learning Models.
Databricks provides an interactive workspace with a Zero-Management cloud platform. It allows Data Analysts, Data Scientists, and Developers to efficiently extract values from large amounts of data.
Furthermore, it easily integrates with third-party applications such as BI (Business Intelligence) and domain-specific tools to provide valuable insights.
Large-scale businesses use this platform for a wide range of tasks, including ETL (Extract, Transform, and Load), Data Warehousing, and Dashboarding Insights for internal and external users.
Today, Databricks is widely used by various enterprise customers to run large-scale production operations across a wide range of industries, including Healthcare, Media and Entertainment, Finance, Retail, and much more.
Key Features of Databricks
Databricks include a variety of features that help users work more efficiently on the Machine Learning Lifecycle. Some of the key features of Databricks include:
- Interactive Notebooks: Databricks’ interactive notebooks offer a variety of languages and tools for accessing, analyzing, discovering new insights, and building new models. The languages that are supported include Python, Scala, R, and SQL.
- Integrations: To make Data Pipelining more structured, Databricks enables integrations with a variety of tools and IDEs (Integrated Development Environment), including PyCharm, IntelliJ, Visual Studio Code, and others. You may also retrieve data in CSV, XML, or JSON format by integrating Databricks with other cloud data storage platforms like Google BigQuery Cloud Storage, Snowflake, and others.
- Access Control: Admins can manage ACL (Access Control Lists) permissions in Databricks to provide them access to Databricks workspace features such as Clusters, Jobs, Notebooks, and Experiments. However, unless an admin updates the ACL permissions, all users will have access to all data and functionality in the Workspace by default.
- Machine Learning features: Databricks offers pre-configured Machine Learning environments based on popular frameworks such as TensorFlow, PyTorch, and Scikit-learn.
To know more about Databricks, visit this link.
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Steps to Set Up Redash Databricks Integration
Now that you have a basic understanding of both the technologies let us go through the procedure of setting up Redash Databricks Integration. Below are the steps you can follow to set up Redash Databricks Integration:
Step 1: Log In to Databricks and Generate a Personal Access Token
The first step in setting up Redash Databricks Integration is to log in to your Databricks account. In case, you don’t have an account, you can sign up for a Databricks account as shown below.
Now, you can follow the below-mentioned procedures to generate a Personal Access Token.
- Navigate to your Databricks workspace and click the Settings Icon in the lower-left corner as shown below.
- Click User Settings as shown below.
- Navigate to the Access Tokens tab and click on Generate New Token as shown below.
- Enter a description (comment) and expiration date if required and then click on Generate as shown below.
- Copy the generated token and keep it safe as this will be required in the later steps.
Step 2: Copy the Connection Details for SQL Endpoint
After you have successfully generated the Personal Access Token, you can follow the below-mentioned procedures to get the connection details for SQL Endpoint.
- Navigate to your Databricks workspace and click the SQL Endpoints icon present in the sidebar.
- Choose an endpoint where you want to connect.
- Navigate to the Connection Details tab and copy the connection details as shown below.
Step 3: Log In to Redash and Select Databricks as a New Data Source
After you have successfully completed step 2, you can log in to your Redash account. In case, you don’t have an account, you can sign up for a Redash account as shown below.
Now, you can follow the below-mentioned procedure to select Databricks as a New Data Source in Redash.
- Click the Settings icon on the Redash Homepage to access the Data Sources management page as shown below.
- Select “Databricks” as the data source from the available options as shown below.
Step 4: Configure the Connection to Set Up Redash Databricks Integration
After you have successfully selected the Databricks as the data source, you will be prompted for the necessary configuration details to set up Redash Databricks Integration as shown below.
- Fill in the necessary configuration details that were copied in steps 1 and 2 as shown below.
You may now run SQL queries on Delta Lake tables as if they were any other Relational data source, and immediately visualize the query results with Databricks as shown below.
With this, you have successfully set up your Redash Databricks Integration. It’s as simple as that.
Benefits of Setting Up Redash Databricks Integration
Some of the benefits of setting up Redash Databricks Integration include:
- Redash Databricks Integration enables Databricks users to shift to a unified Data Analytics platform that can handle any data use case and save significant costs along with increased operational efficiencies.
- Redash Databricks Integration allows you to execute queries and present the results on shareable Dashboards and Visualizations. These results can be visualized in a variety of ways, including graphs, cohorts, and funnels.
- Redash Databricks Integration enables fast query execution for Data Analytics and Data Science without moving data out of the Data Lake.
Frequently Asked Questions (FAQs)
Does Redash use SQL?
Yes, Redash supports all types of data sources including SQL, NoSQL, Big Data, and API. It even provides a powerful online SQL editor for querying data from different sources to answer complex business questions.
Can I use Databricks on-premise?
At the moment, Databricks runs on every major public Cloud. However, they are continuously exploring other deployment scenarios including the on-premise clusters.
Do Databricks interoperate with other Apache Spark distributions?
Yes, Databricks develops a web-based platform for Apache Spark. It runs 100% Spark and hence any application developed on Databricks can run on any distribution compatible with Apache Spark.
What is Hive in Databricks?
Apache Hive is a Data Warehouse project for the analysis of large datasets stored in Hadoop’s HDFS and compatible file systems such as Amazon S3, Azure Data Lake Storage, Google Cloud Storage, etc. It provides an SQL interface for querying data in various Hadoop-compatible databases.
What are Delta and Parquet?
Delta often referred to as Delta Lake, is an open-source storage layer that supports ACID transactions on Apache Spark. Apache Parquet, on the other hand, is an open-source column-oriented data storage format. Delta uses open-source Parquet files to store your data in your Cloud storage. Databricks is optimized for both Parquet and Delta.
In this article, you learned how to set up Redash Databricks Integration. It also gave an overview of Redash and Databricks with their key features. You also learned about the benefits of setting up this integration. You can now create your Redash Databricks Integration to leverage the benefit of both these platforms in one place.
Integrating Redash with Databricks is a fantastic option if you have large volumes of data on Redash waiting to break out of silos and provide valuable insights.
However, Databricks doesn’t provide native connections (like Redash) with other data sources or databases like MySQL, PostgreSQL, MongoDB, etc, and you’ll spend a lot of time migrating data from these sources to Databricks. This is where Hevo comes in.
Replicating the data from various sources into Databricks by writing custom scripts can prove to be a tedious process and will create a slug of errors and data consistency issues.
However, connecting to Databricks using a Data Integration tool like Hevo can perform this process with no effort and no time. Hevo’s No-code Data Pipeline allows you to automatically transform and move data from multiple sources into Databricks for further analysis.
To get a feel of Hevo, you can check out our detailed blog on SQL Server to Databricks migration.
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