Undoubtedly, PostgreSQL is fantastic because of its ability to be used for both OLTP and OLAP data processing needs. And most experienced developers and DBAs prefer working on PostgreSQL because of the high familiarity with its workings, making it a compelling option to use as a data warehouse.
But, for OLAP-style processing, which is purposed for saving historical data and maintaining the ETL processes, PostgreSQL might not be the best fit — because it’s an OLTP database. And for BI functionality, columnar databases like Databricks, Redshift, BigQuery, and Snowflake are the go-to options.
In this post, you will learn two ways to replicate data from PostgreSQL to Databricks. The first method is manual, and the second uses Hevo, a no-code automated data pipeline. Let’s begin.
Why Integrate Postgresql to Databricks?
Let’s start by understanding why there is a need to integrate Postgresql to Databricks. Time Complexity, Features, and data compression are the main factors that play an essential role in doing so.
1) Time Complexity
It may take longer than expected to ensure a seamless PostgreSQL replication effort. Unexpected problems frequently necessitate further study, which might slow down the process of replicating data. Therefore, it is crucial to include time for Ad-hoc research to tackle unforeseen problems in the project timeline from the start.
Your teams can immediately query the data using a “simple-to-use” interface without requiring time-consuming operations. By separating storage from computing and offering limitless scalability, Databricks democratizes data access and improves time complexity.
2) Difference in features on the cloud vs. on-premises
Working with PostgreSQL in the Cloud is different from working with it locally. The Postgresql Cloud databases lack many Postgres extensions, and suppliers sometimes lock some default settings, which restricts Postgres setups and functionality. To assure scalability, several businesses have been forced to go back to on-premises Postgres from Cloud ones.
Databricks is a fully managed solution that supports functionalities like Big data and machine learning. It uses the unified Spark engine to support machine learning, graph processing, and SQL queries. The libraries in Databricks increase developers’ productivity.
3) Data Compression
PostgreSQL stores tables as columns rather than rows. Additionally, as data from the same columns are more likely to be comparable, it aids with data compressing — Postgresql lacks these functionalities.
Databricks support easy and quick access to information. There are no issues with data compression. Databricks also provides additional features for building streaming apps using production-quality machine learning and increasing the use of data science to aid in decision-making. You might want to transfer data from Postgresql to Databricks to implement Databricks features and overcome functionalities.
To overcome these issues, you can transfer data from Postgresql to Databricks.
How to Connect Postgresql to Databricks?
You need to transfer data from Postgresql to Databricks. Let’s understand building this connection using the JDBC driver.
Connecting Postgresql to Databricks using JDBC Driver
- Step 1: Download and install the JDBC driver. Select the destination cluster from the Databricks administration interface by navigating there. Click “Install New” under the Libraries tab.
- Step 2: Choose “Jar” for the library type and “Upload” for the library source. From the installation folder, upload the JDBC JAR file (cdata.jdbc.postgresql.jar).
- Step 3: By referencing the JDBC Driver class and creating a connection string to be used in the JDBC URL, you can connect to PostgreSQL. In the JDBC URL, you also need to set the RTK property (unless you are using a Beta driver). The installation’s licensing file includes instructions on how to set this property, which you can examine.
driver = "cdata.jdbc.postgresql.PostgreSQLDriver"
url = "jdbc:postgresql:RTK=5246...;User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;"
- Step 4: Use the connection string designer integrated with the PostgreSQL JDBC Driver for help creating the JDBC URL. Double-click the JAR file or run it from the command line to start it. The connection string should be copied to the clipboard after you complete the connection properties.
- Step 5: Set the User and Password you want to use to authenticate to the server along with the Server, Port (the default port is 5432), and Database connection settings to establish a connection to PostgreSQL. The data provider connects to the user’s default database if the Database attribute is left empty.
- Step 6: Once the connection has been set up, you can use the JDBC Driver and the connection details to load PostgreSQL data as a data frame.
Challenges Faced While Replicating Data
Setting up pipelines with the help of JDBC Drivers is a technical task. It might not be a good option in these scenarios:
- You don’t have enough engineering bandwidth to set up these pipelines.
- Managing data pipelines across several environments (development, staging, production, etc.) can be expensive. A pipeline’s configuration may alter after being deployed into several settings.
Organizations can use Hevo, an automated pipeline tool, to avoid such challenges. Apart from Postgresql, Hevo helps you transfer data from databases such as MySQL, MongoDB, MariaDB, SQL Server, and much more.
Replicating Data from Postgresql to Databricks using Hevo
You can use Hevo, an automated data pipeline tool, to replicate data from Postgresql to Databricks. Hevo provides users a much simpler way to replicate data from 150+ sources into a data warehouse, database, or a destination of your choice for further analysis.
Hevo provides a consistent and reliable solution to managing data in real-time, ensuring that you always have analysis-ready data.
Steps to Connect Postgresql to Databricks
Below are the simple steps to carry out Postgresql to Databricks using Hevo:
Step 1: Configure Postgresql as a Source
Authenticate and Configure your Postgresql Source. Hevo also supports all the Cloud Postgresql Sources.
Step 2: Configure Databricks as Destination
In the next step, we will configure Databricks as the destination.
Step 3: All Done to Setup Your ETL Pipeline
Once your Postgresql to Databricks ETL Pipeline is configured, Hevo will collect new and updated data from your Postgresql every five minutes (the default pipeline frequency) and duplicate it into Databricks.
Depending on your needs, you can change the pipeline frequency from 5 minutes to an hour.
Data Replication Frequency
|Default Pipeline Frequency||Minimum Pipeline Frequency||Maximum Pipeline Frequency||Custom Frequency Range (Hrs)|
|1 Hr||15 Mins||24 Hrs||1-24|
Within a few minutes, you can set up your Data Pipeline and start replicating data!
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Why Use Hevo to Connect Postgresql with Databricks?
As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.
1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, custom ingestion/loading schedules.
All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software on review sites.
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Here’s how Hevo challenges the normal to beget the new ‘exceptional.’
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- Reliability at Scale – With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
- Monitoring and Observability – Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs
- Stay in Total Control – When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.
- Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
- 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-feature free trial.
- Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in data flow.
Let’s Put It All Together
In this article, you got to learn about the need for Postgresql to Databricks data transfer, as well as got to know about the methods to carry out the process.
Don’t forget to express your experience in the comment section, employing a data pipeline from Postgresql to Databricks using Hevo.
Check out this video to know how Hevo seamlessly replicates data from wide data sources.
Initiate your journey with Hevo today and enjoy fully automated, hassle-free data replication for 150+ sources. Hevo’s free trial gives you limitless free sources and models to pick from, support for up to 1 million events per month, and a spectacular live chat service supported by an incredible 24/7 support team to help you get started.