In the universe of Databricks Lakehouse, Databricks SQL serves as a handy tool for querying and analyzing data. It lets SQL-savvy data analysts, data engineers, and other data practitioners extract insights without forcing them to write code. This improves access to data analytics, simplifying and speeding up the data analysis process.
But that’s not everything Databricks offers. It’s also a great tool for collaboration since it lets team members exchange queries, create simple dashboards, and integrate their work with BI applications such as Power BI, dbt, and Tableau.
If you know what Databricks is, use it regularly, and enhancing collaboration is what you’re looking for, we have some good news: Databricks lakeFS integration lets you add data version control capabilities to your process.
If Databrciks SQL is a mystery to you, keep reading this article to get an in-depth overview of this valuable tool.
What is Databricks SQL?
Databricks SQL (DB SQL) is a tool that provides generic computational resources for SQL queries, visualizations, and dashboards run against Lakehouse databases. Users can create and run such queries, visualizations, and dashboards in DB SQL.
Here are a few key functionalities:
- You can explore schemas and develop, share, and reuse queries using SQL syntax with the built-in SQL editor.
- SQL code snippets can be stored for rapid reuse, and query results can be cached to save run times.
- Query updates can be scheduled to automatically refresh and to provide warnings when significant changes occur in your data.
- You can use visualizations and drag-and-drop dashboards for ad-hoc exploratory research and making sense of data.
DB SQL offers three types of warehouses, each with its own particulars around performance and feature support:
- Classic – it provides basic performance characteristics as well as a restricted range of capabilities.
- Pro – this variant supports more performance features than the traditional, as well as a complete range of capabilities.
- Serverless – it supports all the pro SQL warehouse capabilities as well as advanced Databricks SQL performance features, operating on serverless computing in the customer’s Databricks account.
How Does Databricks SQL Work?
To explain how this solution works, we first need to dive into the Databricks Lakehouse platform. It provides an end-to-end data warehousing solution for any kind of analytics requirement, helping teams accelerate the transition from raw to actionable data at scale and combining batch processing and data streaming if necessary.
Based on open standards and APIs, the Lakehouse platform also enables data teams to easily transition from descriptive to predictive analytics to discover new insights. It combines enterprise data warehouses’ ACID transactions and data governance with the flexibility and cost-efficiency of data lakes.
Key Benefits of Databricks SQL
Cost optimization
By using the serverless warehouse option, teams can cut costs, get the best performance for the lowest price, and eliminate the need to maintain, install, or grow cloud infrastructure.
Integrated governance
You can create a single copy of all your data using open standards and then take advantage of standard SQL to create a consistent governance layer across all data teams.
Extensive ecosystem of tools
To ingest, convert, and query all of your data in-place, you can use SQL and any other tool such as Fivetran, dbt, Power BI, or Tableau together with Databricks.
Dismantling data silos
The tool gives any data practitioner access to the most recent data faster for downstream real-time analyses, enabling a seamless transition from BI to ML.
Easy data ingestion
Data from everywhere can be easily ingested, transformed, and orchestrated using this tool, letting teams work with their data no matter where it’s stored.
Managing everything in one place
Users can manage dependencies and convert data in-place using Lakehouse’s built-in ETL capabilities or their preferred tools on the platform.
Modern analytics and business intelligence
It integrates with the most popular BI tools, including Tableau, Power BI, and Looker. Analysts may use their tool of choice to discover insights based on up-to-date and full data.
No need for resource management
The serverless version removes the need to maintain, install, or grow your cloud infrastructure on the Lakehouse, allowing the data team to focus on their core capabilities.
High speed
It’s built from the bottom up for best-in-class speed; optimize the performance for your tools, query types, and real-world applications.
No more data transfers or data copies in disparate systems
To avoid data lock-in, you can create a single copy of all your data using open format Delta Lake and execute in-place analytics and ETL/ELT on your Lakehouse. Then, using Databricks Unity Catalog, you can quickly locate, secure, and manage all of your data across clouds with fine-grained governance, data lineage, and standard SQL.
What is an SQL Editor?
This type of editor enable users to modify and run SQL statements. They may have the following capabilities:
- Bookmarks, cut, copy, paste, undo, redo, find (and replace)
- Print, save file, uppercase/lowercase keyword highlighting
- Access to commonly used files via auto-completion
- Query-results output editing query-results committing and rolling-back transactions inside cut paper
The requirement of maximizing the value of insights from enterprise data resources made it nearly impossible for a company to settle on a single database management system. When data enters in numerous forms, one platform simply cannot process it efficiently.
Database administrators (DBAs) and data practitioners have long faced challenges while designing and managing business databases in multi-platform systems. Team members must be adaptable and ready to switch between platforms at any time.
And nobody likes to use tools at once. This setup reduces productivity by forcing frequent context switching. Consolidating the toolset helps to reduce the complexity of database development, and this is where integrated development environments (IDEs) come in.
SQL editors are essentially SQL IDEs that enable data teams to run and execute SQL queries of all types. They help to create a link to your database, allowing you to view, control, and alter the data easily.
What are SQL Warehouses and Their Types?
Let’s take a moment to review the basics of SQL data warehouses.
SQL Warehouses (formerly called SQL endpoints) are the computational resources that DB SQL makes use of.
An SQL warehouse is a computing resource that lets you perform SQL commands on SQL data objects. Compute resources are basically infrastructural resources that open the door to efficient cloud-based computation.
This is the connection that will be used to conduct your SQL queries. Users can set the cluster size/family, scalability, and termination parameters.
Databricks offers three warehouse types: Classic, Pro, and Serverless:
- Classic warehouse – provides basic capability and performance.
- Pro warehouse – supports all capabilities and provides greater performance features than the Classic warehouse type, such as query federation, workflow integration, and data science and machine learning functionalities.
- Serverless warehouse – comes with superior performance characteristics and support all of the Pro type’s capabilities, as well as instant and completely managed computing.
Serverless SQL warehouses
In serverless SQL warehouses, the computing layer in serverless compute lives in your Databricks account instead of the AWS account. This provides the account’s users with quick access to fully managed and flexible computing resources.
Note that serverless computing has no impact on how Databricks Runtime clusters interact with notebooks and tasks. Public IP addresses aren’t assigned to serverless SQL warehouses.
To run a serverless SQL warehouse, your Databricks account must be on the platform’s E2 version and it can’t be in free trial mode. Your Databricks workspace must be priced at the Premium or above tier, and be in a Serverless area. Also, you can’t be using an external Hive legacy metastore in your workspace.
Databricks SQL Tools
These tools let you easily write SQL queries and scripts as well as view database objects in Databricks.
Databricks SQL CLI
You can use this command line tool to carry out SQL commands and scripts on an SQL warehouse.
Databricks Driver for SQLTools
This tool lets you interact with SQL warehouses in remote Databricks workspaces using Visual Studio Code.
DataGrip with Databricks integration
To carry out SQL commands and scripts and examine database objects in Databricks, use a query console, schema navigation, smart code completion, and other capabilities.
DBeaver and Databricks integration
This client software application and database management tool allows you to run SQL statements and explore database objects in Databricks.
SQL Workbench for Java
Using this SQL query tool, you may run SQL scripts in Databricks (either interactively or as a batch).
Databricks SQL Best Practices
Databricks offers a number of improvements to suit a wide range of workloads, from large-scale ETL processing to ad-hoc, interactive queries. Many of these optimizations are carried out automatically, as Databricks sets some default parameters.
But there’s still room for improvement, and adjusting configuration parameters can enhance performance in some scenarios.
Tips for boosting Databricks Runtime performance
- Use the most recent Databricks Runtime. In Databricks Runtime 10.4 LTS and later, all of the listed behaviors are by default enabled.
- By loading data to disk volumes attached to computing clusters, disk caching speeds up repeated reads against Parquet data files.
- By bypassing directories that don’t contain data files that meet query predicates, dynamic file pruning improves query speed.
- Use Apache Spark 3.0 to get adaptive query execution, which improves efficiency for a variety of activities.
General recommendations for improving Databricks performance
- Clone tables to create deep or shallow copies of source datasets.
- By using table information, you’ll improve the cost-based optimizer.
- Use Spark SQL to deal with semi-structured JSON data without parsing strings.
- Higher order functions provide built-in, optimized performance for many tasks for which there are no Spark operators – and they outperform user-defined functions in terms of performance.
- For working with complicated data types such as arrays, structs, and JSON strings, Databricks has a variety of built-in operators and specific syntax – take advantage of them!
What is Databricks SQL Analytics?
Databricks SQL Analytics is a tool for performing in-depth SQL data analysis, delivering a single platform for big data, machine learning, and analytics processing. Since it uses familiar SQL syntax, it allows users to do complicated data processing and analysis tasks easily, intuitively, and rapidly. Using it, teams can carry out advanced data analysis and processing operations, discovering insights for data-driven decision making.
The solution is based on Apache Spark, allowing this analytics system to analyze massive data sets thanks to its strong and scalable processing engine.
The tool lets teams manage, analyze, and visualize data using SQL. Users can analyze massive volumes of data quickly and effectively without requiring specialized technical experience or an in-depth understanding of big data technology. As a result, almost anyone with business understanding may get their hands on it without needing technical skills.
Key Aspects of Databricks SQL Analytics
- Integration of the system with numerous data sources, including databases, data warehouses, and cloud storage platforms, to create a data flow pipeline from the system’s initial dashboard to its final dashboard.
- Scalability enables businesses to scale their data processing and analysis activities as their data requirements rise.
- Real-time data streaming and machine learning are examples of advanced data processing and analysis capabilities.
- A user-friendly interface makes sophisticated data processing and analysis activities simple for users of all skill levels.
- Users can carry out complicated data processing and analysis jobs simply, intuitively, and quickly thanks to familiar SQL syntax.
- It’s a useful solution for organizations that want to dip their toes into big data analytics and obtain important insights into their data because of these essential features.
How to Databricks SQL Analytics
Run the First Query
Typically, running your first query is a piece of cake. Navigate the SQL Analytics interface after logging into the Databricks platform. You can start by creating a new question, choosing a data source, and running your first SQL query.
If you know SQL, the syntax will look familiar. You can get insights from your data right away. If you need help getting started, Databricks offers extensive documentation and support resources.
Creating a Query
Go to the SQL Analytics interface and press the “New Query” button. Choose the data source to query and enter your SQL code. You may type your query in the editor and then run it by clicking the “Run” button.
Databricks SQL Analytics has an easy-to-use interface and familiar SQL syntax, making it simple for users of all skill levels to construct and run queries.
Query Filters
Databricks SQL Analytics relies heavily on query filters. They enable you to focus on the most relevant information for your needs by narrowing down the data you are analyzing.
You must include a WHERE clause in your query to establish a filter in Databricks SQL Analytics. For instance, if you need data for a specified date period, you can build a query like this: “SELECT * FROM table WHERE date BETWEEN ‘2023-01-01’ AND ‘2023-09-01′”.
Filters help you locate the information you need to make educated decisions quickly.
Query Parameters
Query parameters allow you to make your queries more flexible and reusable. You may use parameters to design a question that can be executed with varied values without changing the underlying code.
You may change the values in a query without affecting the underlying code. Set a placeholder to create a parameter.
Instead of hard-coding a date range in your query, you might define a parameter for the start date and another for the end date, and then give those values when the query is performed.
Query parameters are handy because they simplify the process of running the same question with various inputs numerous times, saving you time and effort.
Query Snippets
Query snippets are pre-written code that is injected into your queries to save time and effort. SQL statements, functions, and entire queries can all be included in query snippets.
Navigate to the SQL Analytics interface and pick the “Query Snippets” option to use a query snippet. You may then explore the various snippets and select the one you wish to utilize. The chosen snippet will then be included in your query, where you may make changes as needed.
Query snippets are a great way to save time and simplify the query writing process, and they are especially beneficial for users who are new to SQL or the Databricks platform.
The value of lakeFS for DataBricks SQL users
When using Databricks SQL to manage your analytics work, you are provided with best in class tools to collaborate over analytics work. To complement that, you would need to be able to collaborate over the data itself. This is where data version control systems come in, and where lakeFS shines as it supports the Databricks ecosystem out of the box.
Conclusion
Databricks SQL is a great solution that lets users run all of their SQL and BI applications at scale with benefits such as great price/performance, consistent governance architecture, open formats and APIs, and your preferred tool stack. All of that without the risk of vendor lock-in.
If you already use Databricks products and want to introduce data version control, check out this Databricks lakeFS integration tutorial to see how to easily add the open-source data versioning tool lakeFS to your Databricks setup for SQL warehouses.
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