Why Databricks Delta Live Tables?

Name: Why Databricks Delta Live Tables?
Uploaded: 2023-05-08T11:35:41.000Z
Duration: 32 min 43 s

Introduction to Delta Live Tables

In this section, the speaker introduces the topic of Delta Live Tables and emphasizes the importance of understanding the conceptual framework behind it.

Understanding the Background

The speaker encourages viewers to stay with him as he provides a conceptual framework for understanding Delta Live Tables.

Without understanding why something is done or where it comes from, it becomes difficult to be proficient in using it.

Evolution of Big Data Processing

The speaker explains that Delta Live Tables are designed to create end-to-end Databricks pipelines on Databricks and work with Delta tables.

The concept of scaling out big data processing started with Apache Hadoop MapReduce, which used parallel processing across multiple machines.

Apache Spark improved upon Hadoop MapReduce by providing a more usable and scalable platform for big data analytics.

Databricks, founded by the creators of Apache Spark, is a cloud-only platform that wraps around Apache Spark and offers additional services for managing clusters and running workflows.

Differences between Apache Spark and Databricks

While Apache Spark can be run on-premises or as a Platform-as-a-Service (PaaS) like Azure HDInsight, Databricks is only available as a cloud service on Azure, AWS, and Google Cloud.

Databricks provides an IDE and other services that make it easier to develop data science or data warehouse pipelines without requiring low-level work.

Key Features of Apache Spark

This section highlights some key features of Apache Spark that solved previous challenges in big data processing.

Interactive and Language Support

Unlike batch-oriented systems like Hadoop MapReduce, Apache Spark is interactive and allows the use of popular languages such as Python, SQL, R, and Scala.

Scalability and Performance

Apache Spark's scalable architecture enables it to handle large volumes of data efficiently.

It can perform parallel processing across multiple machines to divide and conquer big data pipelines.

Ease of Use

Compared to Hadoop MapReduce, Apache Spark is more user-friendly and intuitive.

It eliminates the need for extensive Java coding and provides a platform that is easier to work with for complex solutions.

Integration with Databricks

Databricks, being built by the creators of Apache Spark, seamlessly integrates with Apache Spark.

It offers additional services and features that enhance the usability and functionality of Apache Spark.

Conclusion

The speaker concludes by summarizing the benefits of using Delta Live Tables on Databricks.

Delta Live Tables on Databricks provide an end-to-end solution for creating data pipelines.

They leverage the power of Apache Spark while offering a user-friendly interface through Databricks' cloud platform.

By understanding the background and key features of Delta Live Tables, users can effectively utilize this technology for their data processing needs.

Introduction to Distributed Data Sets and Data Lakes

This section introduces the concept of distributed data sets and the popularity of data lakes. It also highlights the challenges faced with data governance and limited functionality in Apache Spark.

Distributed data sets have limitations, but they became popular with the rise of data lakes.

Data lakes often lack proper data governance, leading to uncontrolled storage of various types of data.

Apache Spark's formats do not allow for easy modification of data, only enabling read-on-demand operations.

The need for functionality similar to traditional relational data warehouses led to the development of Delta Lake.

Delta Lake: Features and Benefits

This section explains how Delta Lake addresses the limitations faced with traditional data lakes by providing CRUD operations, ACID transactions, and SQL-like relational tables.

Delta Lake is an open-source project that offers CRUD operations and ACID transactions.

It provides SQL-like relational tables with features such as joins, merge for updates, inserts, deletes, and transaction logging.

Delta Lake combines the benefits of a scalable data warehouse with familiar relational database functionality.

However, building ETL pipelines for Delta Lake can be complex due to dealing with streaming data sources and real-time processing.

Introducing Delta Live Tables

This section introduces Delta Live Tables as a solution to simplify ETL pipeline development by automating maintenance, monitoring, and development processes.

Delta Live Tables aim to eliminate complexity in building custom frameworks and one-off designs for ETL pipelines.

They provide automatic ETL maintenance, monitoring, and almost automated development of pipelines.

Best use cases include streaming scenarios and auto-drop file ingestion into a large-scale data warehouse.

Complex ETL processes involving multiple feeds from ingestion to aggregated data are also suitable use cases.

Limitations and Requirements of Delta Live Tables

This section highlights the limitations and requirements of using Delta Live Tables, including its exclusivity to Databricks and support only for Delta tables.

Delta Live Tables are a proprietary service exclusive to Databricks.

While open-source Spark supports Delta Lake tables, it does not support Delta Live Tables.

Large data volumes and complex ETL processes are recommended use cases for Delta Live Tables.

It is important to note that only Delta tables can be used with this service, limiting compatibility with other data formats.

Data Flow in a Traditional Pipeline vs. Using Delta Lake

This section compares a traditional pipeline built from scratch with one utilizing Delta Lake's capabilities.

A traditional pipeline involves manually writing code to read streaming data from event hubs or Kafka into a database.

In contrast, using Delta Lake, the data is streamed into a Delta table for transactional storage.

The diagram illustrates a simple data flow pipeline that can be built on any platform, not limited to Databricks.

Timestamps were associated with bullet points as requested.

Data Pipeline Overview

In this section, the speaker discusses the process of building a data pipeline and highlights the steps involved in storing and cleansing data.

Storing Raw Data

Raw data from event hubs is stored in the raw storage layer without any cleanup or modifications.

The data is stored in tables without performing any fancy operations.

Data Cleansing

The next step involves cleansing the data by removing null values and eliminating bad values.

The cleansed data is then stored in a separate table.

Merging Sales Data

The speaker mentions merging storage sales with online sales to create a unified sales table.

This merge operation allows for combining different sources of sales data into one table.

Validation Rules

After merging the sales data, validation rules need to be defined and applied to ensure data integrity.

Code is written to identify violations of these rules and handle them appropriately.

Querying Sales Table

Once the validation process is complete, the sales table is ready for querying at a granular level.

This table serves as a foundation for further analytics and reporting.

Aggregating Sales Data

To handle large volumes of data for analytics purposes, it may be necessary to perform aggregation on the sales table.

A summarized version of the table, called aggregated sales, can be created for efficient analysis.

Including External Files in Data Warehouse

In this section, the speaker explains how external files can be included in a data warehouse using a folder-based pipeline approach.

Vendor File Integration

External vendor files, such as vendor invoices, are dropped into a designated folder.

A polling process monitors this folder and triggers actions when new files are detected.

Cleansing Vendor Invoices

When a new vendor file is detected, it gets pulled into the pipeline for testing and cleansing.

The cleansed vendor invoices are then integrated into the data warehouse.

Aggregated Vendor Invoices

Instead of creating a separate detail table, the vendor invoices are directly merged into the existing structure.

This process requires a polling mechanism to track new file arrivals.

Delta Live Tables

In this section, the speaker introduces Delta live tables as an alternative approach to building data pipelines on Databricks.

Live Table Declaration

To utilize Delta live tables, code modifications are required to indicate that each table created is a live table.

The keyword "live" is used in the code declaration to inform Databricks about the nature of the table.

Databricks Automation

When a live table is declared, Databricks takes over the responsibility of maintaining it.

Databricks performs various tasks behind the scenes, such as mapping data sources and destinations.

Autoloader Integration

Autoloader, although not exclusive to Delta live tables, works seamlessly with them.

It monitors folders for events like new file arrivals and automatically triggers pipeline processes.

Additional Features

Delta live tables offer automatic checkpoints and restarts for improved reliability.

They provide built-in validation rule enforcement and handle errors and failures efficiently.

Data lineage tracking allows users to trace data origins and destinations.

Schema evolution support enables easy incorporation of new columns or changes in data structure.

Optimization and cluster management features optimize performance based on workload requirements.

Delta Live Tables in Databricks

In this section, the speaker discusses Delta Live Tables in Databricks and their integration with the workflow engine.

Delta Live Tables Integration

Delta Live Tables are currently only available in Databricks and not open-sourced.

The integration of Delta Live Tables with the workflow engine makes it difficult to open source them.

When creating Delta Live Tables, workflows and jobs are involved.

Notebooks for Delta Live Tables cannot be run interactively.

Data needs to be analyzed by Databricks before building the real pipeline.

Limitations of Running Delta Live Table Notebooks Interactively

This section highlights the limitations of running Delta Live Table notebooks interactively.

Limitations

It is not possible to run Delta Live Table notebooks interactively.

Code can be created in notebooks, but they cannot be executed interactively.

Databricks requires time to analyze live table definitions before building the pipeline.

Conclusion

The speaker emphasizes that Delta Live Tables are currently only available in Databricks and discusses their integration with the workflow engine. They also mention that running Delta Live Table notebooks interactively is not possible due to the need for analysis by Databricks.