How to use external table in Databricks?

External tables are a powerful feature in Databricks that allow you to access and query data stored outside of the platform. In this article, we will explore the concept of external tables, how to set up Databricks for external tables, and the various operations you can perform on external tables.

Understanding the Concept of External Tables

External tables in Databricks provide a way to map data stored in external systems to a schema in Databricks. They do not physically store the data within Databricks, but rather serve as a metadata layer that defines the structure and location of the external data. This approach enables you to seamlessly work with data residing in different storage systems, such as S3, Azure Blob Storage, or Hadoop Distributed File System (HDFS), without having to move or import the data into Databricks.

Definition of External Tables

An external table is a table object in Databricks that references data residing outside of Databricks. It contains a schema definition that specifies the columns and their data types, as well as the location of the external data. By defining an external table, you can access and query the data without physically transferring it into Databricks. This decoupling of data storage and compute allows for greater flexibility and scalability in data processing workflows.

Importance of External Tables in Databricks

External tables play a crucial role in enabling seamless data integration and analysis across different systems. They provide a unified view of data from disparate sources, eliminating the need for data duplication and centralizing data management. With external tables, you can leverage the power of Databricks to perform complex data transformations and analytics on data stored in various external storage systems, accelerating insights and decision-making processes.

One of the key benefits of using external tables in Databricks is the ability to leverage the existing data infrastructure and investments. Organizations often have data stored in different systems and formats, making it challenging to consolidate and analyze the data effectively. With external tables, you can directly access and query data from these external systems, without the need for data movement or duplication. This not only saves time and resources but also ensures data integrity and consistency.

Furthermore, external tables enable seamless collaboration and data sharing across teams and departments. Since the external tables reference the data residing in external systems, multiple users can access and analyze the same data simultaneously. This promotes cross-functional collaboration and enables different teams to leverage the same data for their specific use cases, without the need for data replication or manual data transfers.

Setting up Databricks for External Tables

Before you can start using external tables in Databricks, there are a few prerequisites that you need to fulfill.

Prerequisites for Using External Tables

Firstly, you need to have access to a Databricks workspace and an external storage system where your data is stored. This could be an S3 bucket, an Azure Blob Storage container, or an HDFS directory. Make sure you have the necessary credentials and permissions to access the external storage system.

Secondly, you need to configure the Databricks environment to connect to the external storage system. This involves setting up connectivity options, such as mounting the external storage system as a Databricks file system (DBFS), or configuring access keys and secrets.

Once you have fulfilled these prerequisites, you can proceed with setting up external tables in Databricks. External tables allow you to access and query data stored in external storage systems, without having to physically move the data into Databricks.

When setting up external tables, it is important to define the schema of the data. This includes specifying the column names, data types, and any constraints or transformations that need to be applied. Databricks supports various file formats for external tables, such as Parquet, CSV, JSON, and Avro.

In addition to defining the schema, you also need to specify the location of the data in the external storage system. This could be the path to a specific file or a directory containing multiple files. Databricks provides options to specify the file format, compression codec, and other properties of the data.

Once the external table is set up, you can start querying the data using SQL or DataFrame APIs in Databricks. The queries will be executed on the data stored in the external storage system, without the need to import or copy the data into Databricks.

It is worth noting that external tables in Databricks provide a convenient way to work with data that is already stored in external storage systems. They offer flexibility, scalability, and cost-effectiveness by eliminating the need to duplicate or move data. By leveraging the power of Databricks, you can easily analyze and derive insights from your data, regardless of where it is stored.

Creating an External Table in Databricks

Once you have fulfilled the prerequisites, you can proceed to create an external table in Databricks. Creating an external table allows you to access and query data that resides outside of Databricks, such as in an external storage system like Amazon S3 or Azure Blob Storage. This can be particularly useful when you want to analyze large datasets without having to physically move the data into Databricks.

Now, let's dive into the step-by-step process of creating an external table in Databricks:

Step-by-Step Guide to Create an External Table

To create an external table, you need to define the schema and specify the location of the external data. Here's a detailed breakdown of the process:

1. Define the schema: Determine the columns and their corresponding data types that will make up the external table. This step is crucial as it ensures that the table's structure matches the structure of the external data. You can define the schema using the SQL CREATE TABLE statement or the DDL (Data Definition Language) syntax in Databricks.
2. Specify the location: Identify the location of the external data and specify it in the external table definition. This could be a file path or a directory path in the external storage system. It's important to double-check the location specified to ensure that it points to the correct data source.
3. Create the external table: Execute the SQL CREATE TABLE statement or the DDL syntax to create the external table in Databricks. This step registers the table object in Databricks, referencing the external data. Once the table is created, you can start querying it just like any other table in Databricks.

By following these steps, you can seamlessly integrate external data into your Databricks environment and leverage its powerful analytics capabilities.

Common Mistakes to Avoid When Creating External Tables

While creating external tables, it's important to be aware of potential pitfalls that can lead to errors or data inconsistencies. Here are some common mistakes to avoid:

• Incorrect schema definition: Ensure that the schema you define matches the structure and data types of the external data. Mismatched schemas can lead to data inconsistency or query failures. It's recommended to carefully review the external data's schema before defining the schema for the external table.
• Invalid location specification: Double-check the location specified for the external data. An incorrect path can result in the table not being able to access the data. It's always a good practice to validate the location and ensure that it points to the correct data source.
• Incomplete or missing access credentials: Make sure you provide the necessary access credentials, such as access keys or secrets, to enable Databricks to retrieve the data from the external storage system. Without the proper credentials, Databricks won't be able to access the external data, rendering the table useless.

By avoiding these common mistakes, you can ensure a smooth and error-free process when creating external tables in Databricks. Now that you have a comprehensive understanding of the steps involved and the potential pitfalls to watch out for, you're ready to harness the power of external data in your Databricks environment.

Managing Data in External Tables

Once you have created an external table in Databricks, you can perform various operations to manage the data within the table.

Inserting Data into External Tables

Inserting data into external tables involves loading data from Databricks or external systems into the table. You can use standard SQL INSERT statements or Databricks utilities to perform bulk data loading. It's important to ensure data consistency and integrity while performing data inserts, especially if the external table is being used by multiple users or processes simultaneously.

Updating Data in External Tables

Updating data in external tables allows you to modify existing records or columns within the table. You can use SQL UPDATE statements to perform individual or batch updates on the external data. It's worth noting that external tables might have limitations on update operations depending on the specific external storage system being used.

Deleting Data from External Tables

Deleting data from external tables removes specific records or entire partitions from the table. You can use SQL DELETE statements to delete data based on certain conditions or criteria. It's essential to exercise caution while deleting data, as the operation cannot be undone, and irreversible data loss may occur.

Querying Data from External Tables

One of the key benefits of using external tables in Databricks is the ability to query data residing in external storage systems.

Basic Querying Techniques

To query data from external tables, you can use standard SQL SELECT statements in Databricks. You can apply various filtering, aggregation, and join operations to retrieve the desired data. It's recommended to optimize queries by leveraging partitioning and indexing techniques to improve query performance, especially when dealing with large datasets.

Advanced Querying Techniques

In addition to basic querying techniques, Databricks offers advanced features and optimizations for querying external tables. For example, you can leverage Databricks Delta Lake, a powerful transactional storage layer, to improve query and data management capabilities. You can also leverage Databricks Runtime optimizations, such as predicate pushdown and column pruning, to further enhance query performance.

Conclusion

In this article, we have explored the concept of external tables in Databricks, their importance in data integration, and how to set up Databricks for using external tables. We have also covered the process of creating external tables, managing data within the tables, and querying data from external tables. By leveraging external tables in Databricks, you can break down data silos, accelerate data analysis, and gain valuable insights from diverse data sources.