SQLite Databricks Integration: 2 Easy Steps

SQLite is one of the most widely used open-source Relational Database Management Systems in the world. SQLite uses the Structured Query Language (SQL) to define, update, and query data in Databases. However, SQLite is best suited for smaller Databases, and as your business scales, you might want to move data from SQLite to Databricks to explore your business data.

Databricks is a Cloud-based Data Engineering platform founded by the creators of Apache Spark. It is widely used by businesses to seamlessly store, transform, and visualize large amounts of data from various sources. It is a multi-functional tool that also allows companies to develop Machine Learning models and perform interactive analyses. This article provides you with a step-by-step guide to establishing an SQLite Databricks integration for your business.

But before getting into “SQLite Databricks integration”, let’s discuss both the robust platforms in brief.

What is SQLite?

SQLite is an open-source Relational Database Management System (RDBMS). Most Relational Databases are based on the Client-Server model, which means that the Database needs a server to run on. SQLite, on the other hand, is a Serverless Relational Database Management System, also known as an Embedded Database. SQLite Database is used to access data from within the software. The Database can be accessed directly without the need for a Host Server intermediary.

SQLite is a file-based, self-contained Database that is known for its portability, low-memory performance, and high reliability. It is easy to set up and is designed to work without a Database Administrator. SQLite data transactions are ACID-compliant (Atomicity, Consistency, Isolation, and Durability). SQLite, being an open-source tool, is available free of cost for all users. However, you can always pay for extra extensions, depending on the use case.

When to Use SQLite?

One of SQLite’s greatest advantages is that it can run on all platforms, including macOS, Windows, Linux, etc. SQLite is an RDBMS contained in a C library, hence, SQLite may be used by applications written in any language as long as they can link to external C libraries. Below are the appropriate uses for SQLite.

• SQLite is useful for creating Embedded Software for digital devices such as Televisions, Phones, Set-Top Boxes, Game Consoles, Cameras, and so on.
• Its flexibility allows you to work on various Databases in the same session, depending on your needs.
• It is used as a Temporary Dataset that allows applications to process data.
• It is a cross-platform DBMS, hence you can access it over all platforms including Windows, macOS, and more.
• It works well as a Database Engine for most websites, as it can manage low to medium-traffic HTTP requests.
• Educational institutions utilize it for learning and training purposes because it is simple to set up and use.

What is Databricks?

Databricks is a popular Cloud-based Data Engineering platform developed by Apache Spark. It deals with large amounts of data and allows you to easily extract insights from it. With the main focus on Big Data and Analytics, it also assists you in the development of AI (Artificial Intelligence) and ML (Machine Learning) solutions. Machine Learning libraries such as Tensorflow, Pytorch, and others can be used for training and developing Machine Learning models.

Databricks offers an interactive workspace with a Zero-Management Cloud platform that allows Data Analysts, Data Scientists, and Developers to efficiently interact and extract valuable insights from large amounts of siloed data. DataFrames and Spark SQL libraries can be used to interact with structured data. Furthermore, it can be easily integrated with third-party applications such as BI (Business Intelligence) tools to provide valuable insights.

Databricks is widely used across a wide range of industries, including Healthcare, Media and Entertainment, Finance, Retail, etc., to run large-scale production operations.

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 provide users with a variety of languages (such as Python, Scala, R, and SQL) and tools for accessing, analyzing, and extracting new insights.
• Integrations: Databricks can be easily integrated with a variety of tools and IDEs (Integrated Development Environment), including PyCharm, IntelliJ, Visual Studio Code, etc., to make Data Pipelining more structured.
• Multiple Data Formats: Users can retrieve data in various formats such as CSV, XML, or JSON by integrating Databricks with other Cloud data storage platforms like Google BigQuery Cloud Storage, Snowflake, and others.
• Optimized Spark Engine: Databricks allows you to avail the most recent versions of Apache Spark. Equipped with the availability and scalability of multiple Cloud service providers, it is very easy to set up clusters and build a fully managed Apache Spark environment.
• Machine Learning features: Databricks offers pre-configured Machine Learning libraries based on popular frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Delta Lake: Databricks houses an open-source Transactional Storage layer that can be used for the whole data lifecycle. This layer brings data scalability and reliability to your existing Data Lake.

Why is SQLite Databricks Integration Important?

SQLite is best suited for smaller Databases and it does not have any specific user management functionality, making it difficult for users to collaborate. It’s not easily scalable and lacks customizability. On the other hand, Databricks deals with large amounts of data and allows you to easily visualize it and extract insights from it. Therefore, it is highly recommended to migrate from SQLite to Databricks if you’re dealing with large volumes of data.

The SQLite Databricks integration allows you to get the most out of both SQLite and Databricks. SQLite’s independent & reliable Database allows you to store your daily business transactions. SQLite’s low memory environment and unlimited working model provide you the flexibility you need to enhance your data-driven projects. In addition, you can leverage the SQLite Databricks integration to explore your business data using self-service Data Analytics tools and Machine Learning options. 

You can further use the SQLite Databricks integration for Business Intelligence & Analytics to create charts, graphs, and reports, ETL & Replication to move and replicate data, Data Management, and Workflow Automation to build automated workflows.

Establishing SQLite Databricks Integration via CSV Files

Depending on the requirements, one might want to leverage SQLite Datbricks integration for their business. When integrated with SQLite, data can be moved from SQLite to Databricks to perform almost real-time analysis to solve some of the biggest data problems for businesses. This article will help you to manually establish an SQLite Databricks integration.

While setting up the SQLite Databricks connection manually, you need to convert your SQLite data into CSV and then transfer it to a Databricks Table. To achieve this SQLite Databricks integration, follow the easy steps given below.

• Step 1: Convert SQLite Data to CSV Files
• Step 2: Loading CSV Data into Databricks Table

Step 1: Convert SQLite Data to CSV Files

The command-line utility sqlite3 or sqlite3.exe can be used to convert SQLite data to CSV files. Follow the easy steps below to get started.

• Use the .header command to enable the result set’s heading when converting SQLite data to CSV files.
• Now, you can change the sqlite3 tool’s output mode to CSV to get the result in CSV format.
• Save the result as a CSV file. Select the data table from which you want to retrieve the information and run the sample query as shown below.

>sqlite3 c:/sqlite/chinook.db
sqlite> .headers on
sqlite> .mode csv
sqlite> .output data.csv
sqlite> SELECT customerid,
   ...>        firstname,
   ...>        lastname,
   ...>        company
   ...>   FROM customers;
sqlite> .quit

• Upon this, a data.csv will be created.

• Alternatively, you can also use the sqlite3 tool’s options to convert SQLite data into CSV Format.

>sqlite3 -header -csv c:/sqlite/chinook.db "select * from tracks;" > tracks.csv

• You can also execute your statements in query.sql and send data to a CSV file if you have a file named query.sql containing the script to query data.

>sqlite3 -header -csv c:/sqlite/chinook.db < query.sql > data.csv

Step 2: Loading CSV Data into a Databricks Table

The next step of the SQLite Databricks connection requires you to load the exported SQLite CSV data into a Databricks Table. Follow the below-mentioned steps to easily import CSV files to Databricks.

• Launch Databricks and navigate to the sidebar menu. Click on the “Data” option.
• Now, click on the “Create Table” button.
• Click on the dropdown and browse the CSV file that you want to upload. Alternatively, you can also drag the required CSV file to the Files Dropzone.
• After uploading the file, the path would look something like this: /FileStore/tables/<filename>-<integer>.<file-type>.

• Now, click on the “Create Table with UI” button.
• The data you added to a table with the Create Table UI is also accessible via the landing page’s Import & Explore Data section.

Now that you have successfully uploaded data to the table, you can follow the steps given below to modify and read the data in order to perform Databricks Read CSV:

• Now, to read the uploaded data, select a Cluster to preview the table and click on the “Preview Table” button.
• The table attributes are of type “String” by default. You can select the appropriate data type for the attributes from the drop-down menu. The left bar consists of various options to update the data in the table.
• Once you have updated the data and the configurations, click on the “Create Table” button.
• Now, navigate to the “Data” section and choose the Cluster where you have uploaded the file to read data.

You’ve now successfully established the SQLite Databricks integrations. You’re now all set to deep dive into your business data and perform insightful analysis using Databricks.

However, going ahead with this method, you will be stuck with data inconsistencies and errors as Data Transformation is a tedious task in this case. You can opt for a third-party solution if you don’t want to spend a lot of time resolving data issues.

Conclusion

Integrating SQLite with Databricks is a fantastic option if you have large volumes of data on SQLite waiting to break out of silos and provide valuable insights. SQLite Databricks integration allows companies to simplify and streamline their storage of data in modern Data Warehouses. This also allows organizations to explore their business data with the help of self-service analytical tools and Machine Learning options.

This article provides you with a step-by-step guide on how to establish an SQLite Databricks integration. Replicating the SQLite data into Databricks via CSV files is 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.

visit our website to explore hevo

Hevo Data with its strong integration with 100+ Sources allows you to not only export data from multiple sources & load data to the destinations like Databricks, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out!

Share your experience of establishing an SQLite Databricks connection in the comments section below.