AWS Glue Vs Databricks: ETL Services Comparison

Here are the key items Databricks Vs AWS Glue described with use cases. A right way to understand these ETL services.

Databricks Vs AWS Glue Key Items Differences

AWS Glue is a fully managed extract, transform, and load (ETL) service. It simplifies the process of preparing and loading data for analytics. It provides a serverless environment in which to execute data integration tasks. Here are key terms in AWS Glue.

1. Crawler

• Definition: A crawler is a component in AWS Glue. It connects to a data store and automatically discovers the structure of the data. It creates or updates metadata tables in the AWS Glue Data Catalog.
• Use Case: Automatically detect data schema. These include tables, columns, and types. Store this metadata in the Data Catalog for later ETL processing.

2. Data Catalog

• Definition: The AWS Glue Data Catalog is a central metadata repository. It stores information about data sources, their schema, and how they should be accessed.
• Use Case: Acts as a unified repository for metadata. It allows AWS services like Athena, Redshift, and EMR to easily access and query the data.

3. Database

• Definition: In AWS Glue, a database is a logical grouping of tables. It is a metadata container that stores schema information for the tables it contains.
• Use Case: Organizes and categorizes data sources, making managing and accessing related datasets easier.

4. Table

• A table in AWS Glue holds schema information about data stored in a data store. This includes data stored in S3 or RDS. The schema information includes column names, data types, and partition keys.
• Use Case: Defining the data structure for ETL jobs to guarantee smooth data access and manipulation.

5. Connection

• Definition: A connection in AWS Glue is a set of parameters that define how to connect to a data store. It includes host, port, user credentials, and encryption settings.
• Use Case: Facilitates secure and reliable connections to various data stores like Amazon RDS, Redshift, or external databases.

6. Job

• Definition: An AWS Glue job is a task that you define to perform an ETL operation. Jobs can be written in Python or Scala and run on a serverless Apache Spark environment.
• Use Case: Automates the process of extracting, transforming, and loading data into a target data store.

7. Job Script

• Definition: The script is the code. It is usually written in Python or Scala. This code defines the ETL process in an AWS Glue job. It specifies the data sources, transformations, and destinations.
• Use Case: Allows customization of ETL logic using a familiar programming language, enabling complex data transformations and processing.

8. Transformations

• Definition: Transformations in AWS Glue are operations applied to the data to modify, filter, or aggregate it. Examples include mapping, filtering, joining, and splitting data.
• Use Case: Used to clean and shape the data according to analytical needs. This includes converting formats, removing duplicates, or enriching data.

9. Trigger

• Definition: A trigger is used to start AWS Glue jobs based on specific events or schedules. Triggers can be time-based (scheduled) or event-based (on-demand).
• Use Case: Automates the execution of ETL jobs. This allows them to run at specified intervals or in response to certain events.

10. Dev Endpoint

• Definition: A development endpoint is an environment where you can interactively develop and debug AWS Glue scripts. It provides an Apache Zeppelin notebook interface to write and test code.
• Use Case: Allows data engineers to experiment with ETL scripts and verify their functionality before running them in production.

11. Classifier

• Definition: A classifier in AWS Glue helps pinpoint the format and structure of data files. It can recognize common file types like CSV, JSON, Parquet, and XML. You can define custom classifiers for other formats.
• Use Case: Enhances the data discovery process. It allows AWS Glue to correctly interpret and parse various data formats during crawling.

12. Partition

• Definition: Partitions are a way of dividing a table into distinct parts. They are based on the values of one or more columns. AWS Glue can read partitioned data more efficiently.
• Use Case: Speeds up query processing. This is achieved by allowing AWS Glue to read only the relevant partitions. This reduces the amount of data scanned.

13. Bookmark

• Definition: Bookmarks in AWS Glue track processed data. This ensures that ETL jobs do not reprocess the same data in future runs.
• Use Case: Enables incremental data processing by keeping track of the last processed data point. This ensures efficiency and avoids data duplication.

14. Dynamic Frame

• Definition: A DynamicFrame in AWS Glue is akin to a DataFrame in Spark but with extra metadata and features. It can handle nested data and offer advanced transformations.
• Use Case: Offers a more flexible and schema-aware structure for performing ETL operations on semi-structured and structured data.

15. Job Bookmarking

• Definition: Job bookmarking is a feature that enables AWS Glue jobs to keep track of earlier processed data. This feature prevents reprocessing.
• Use Case: Useful for processing streaming or incremental data. This ensures that only new or changed data is processed in each job run.

16. Script Editor

• Definition: The Script Editor is an interactive interface in AWS Glue Console where you can create and edit job scripts.
• Use Case: Provides an integrated environment to write, test, and debug ETL scripts directly within the AWS Glue Console.

17. Workflow

• Definition: A workflow in AWS Glue is a collection of triggers, crawlers, and jobs. These are run in a specified order. It provides a way to automate and manage complex data pipelines.
• Use Case: Coordinates the execution of multiple ETL jobs and other tasks. It provides a way to manage complex data processing workflows.

AWS Glue’s components and features make it a powerful tool. It is great for building, automating, and managing ETL processes in a serverless environment.

Databricks is an integrated data analytics platform built on Apache Spark. It offers tools for big data processing, machine learning, and data analytics. Here are key terms and components in Databricks.

1. Workspace

• Definition: The workspace is the primary user interface for Databricks. It organizes all the resources notebooks, libraries, dashboards, and jobs in folders.
• Use Case: Provides an environment for users to create, organize, and collaborate on various data analytics and machine learning projects.

2. Notebook

• Definition: A notebook is an interactive environment. You can write and execute code in languages viz. Python, Scala, R, SQL, and Markdown.
• Use Case: Used for data exploration, visualization, transformation, and creating machine learning models. Notebooks allow for easy collaboration and sharing of results.

3. Cluster

• Definition: A cluster is a set of compute resources used to run Databricks workloads, including notebooks, jobs, and libraries. Clusters can be created on-demand and scaled automatically.
• Use Case: Provides the computational power for processing large datasets and running analytics or machine learning tasks. Supports both interactive and automated workloads.

4. Job

• Definition: A job is a way to run a notebook. It can also run a JAR/Python script on a scheduled or on-demand basis in Databricks. Jobs can be scheduled to run periodically or triggered by events.
• Use Case: Automates the execution of data pipelines, ETL processes, or machine learning models, allowing for repeatable and scheduled tasks.

5. Library

• Definition: A library is a package or module that can be imported into a Databricks workspace. Libraries can include third-party packages from PyPI, Maven, or custom JARs and Python scripts.
• Use Case: Extends the functionality of Databricks notebooks and jobs. It lets you use extra libraries and tools for data processing, visualization, and machine learning.

6. DBFS (Databricks File System)

• Definition: DBFS is a distributed file system mounted on Databricks clusters. It lets you store and access files and data across different clusters.
• Use Case: Used for storing data files, libraries, and other resources needed for analytics and machine learning tasks. Provides a convenient way to persist data across sessions.

7. Delta Lake

• Definition: Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to big data workloads. It provides versioned, time-traveling data with support for schema enforcement and evolution.
• Use Case: Used to build reliable data lakes with features like data versioning and upserts. It also supports real-time data processing. These features improve data quality and reliability.

8. Unity Catalog

• Definition: Unity Catalog is a unified governance solution for managing data assets, permissions, and metadata across Databricks workspaces. It provides a centralized metadata store with fine-grained access control.
• Use Case: Used to manage, govern, and discover data securely across different workspaces, enhancing collaboration and compliance.

9. Workspace API

• Definition: The Workspace API allows programmatic interaction with the Databricks workspace. It enables operations like managing notebooks, libraries, and other resources using REST API calls.
• Use Case: Automates and integrates workspace management into other systems or CI/CD pipelines, allowing efficient resource management.

10. SQL Analytics

• Definition: SQL Analytics is a Databricks feature. It provides a workspace and a set of tools for running SQL queries. These queries are executed against data stored in Delta Lake or other data sources(supported).
• Use Case: Enables data analysts to create, run, and share SQL queries and dashboards. This provides insights into large datasets. It does not need deep knowledge of Spark.

11. Databricks Connect

• Definition: Databricks Connect is a client library. It lets you connect your favorite IDE or local environment to a Databricks cluster. It runs code on Databricks from outside the platform.
• Use Case: Provides a seamless development experience. It lets you use familiar tools. You can also take advantage of Databricks’ scalable compute resources.

12. Cluster Pools

• Definition: Cluster Pools allow you to create and manage pools of instances that can be used to quickly launch clusters. They reduce cluster startup times by maintaining a set of idle, ready-to-use instances.
• Use Case: Speeds up the creation of new clusters. This is especially important in environments with high cluster demand. It improves productivity and reduces costs.

13. Databricks Runtime

• Definition: The Databricks Runtime is the set of core components that provide the computing environment on Databricks clusters. It includes Apache Spark, optimized libraries, and pre-installed packages for data processing and machine learning.
• Use Case: Offers a highly optimized and pre-configured environment for running analytics and machine learning workloads. This setup simplifies usage. It also improves performance.

14. Table ACLs (Access Control Lists)

• Definition: Table ACLs allow you to set fine-grained permissions on tables within Unity Catalog. They control which users can access, modify, or share data.
• Use Case: Enhances data security and governance. Administrators can control access to sensitive data at the table or column level.

15. Secrets Management

• Definition: Secrets Management in Databricks provides a secure way to store and access sensitive information. This includes passwords, API keys, and database connection strings. Secrets are stored in a secure vault and can be accessed in notebooks and jobs.
• Use Case: Protects sensitive information by securely managing credentials and other secret data, reducing the risk of exposure.

16. MLflow

• Definition: MLflow is an open-source platform integrated into Databricks for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment.
• Use Case: Used for tracking experiments. It is also used for packaging machine-learning models and deploying them to production. This provides a standardized workflow for ML projects.

17. Repos

• Definition: Repos allow users to integrate Databricks notebooks with Git repositories. This feature enables version control, collaboration, and CI/CD workflows.
• Use Case: Facilitates code collaboration and versioning. Users can pull, push, and merge changes from Git repositories. This can be done directly within the Databricks workspace.

18. Photon

• Definition: Photon is a vectorized query engine for Apache Spark, optimized for the Databricks Runtime. It provides improved performance for SQL and DataFrame queries.
• Use Case: Enhances query performance and reduces the cost of running analytics workloads on Databricks, especially for large-scale data processing.

19. SQL Endpoint

• Definition: SQL Endpoints offer a connection point for running SQL queries on data stored in Delta Lake. It lets you create endpoints that serve SQL queries for BI tools and applications.
• Use Case: Enables data analysts and BI tools to query data in Delta Lake using SQL. This provides a low-latency interface for interactive analytics.

20. Event Logs

• Definition: Event Logs capture and store events within the Databricks workspace. These events include cluster events, job executions, and notebook activities.
• Use Case: Used for monitoring, auditing, and troubleshooting Databricks activities, providing insights into the platform’s usage and performance.

21. AutoML

• Definition: AutoML in Databricks provides an automated machine-learning feature that helps you quickly develop machine-learning models. It automates feature engineering, model training, and hyperparameter tuning.
• Use Case: Simplifies the machine learning model development process. It makes it accessible to non-experts. It also speeds up time-to-value for ML projects.

22. Data Engineering

• Definition: Data Engineering in Databricks refers to building and maintaining data pipelines for processing large-scale datasets. It involves using Spark and other tools to ingest, transform, and store data.
• Use Case: Provides a scalable and flexible environment for building ETL workflows. It processes batch and streaming data. It also prepares data for analytics.

Databricks provides a comprehensive platform for data processing, analytics, and machine learning. It offers tools and features that enhance productivity, collaboration, and scalability for data-driven projects.