Captain to Finisher: The Key Players in Databricks Delta’s Ecosystem

When I first started using Databricks, I was completely lost in the world of Delta tools: Delta Lake, Delta Tables, Delta Live Tables (DLT), Delta Engine, Delta Sharing, Delta Transaction Log (DTL), and Delta Merge. I kept wondering, Do I really need all of these? What do they even do? 

Think of this blog as a cricket match strategy. Just like every cricket player has a crucial role to play, each tool in the Databricks Delta Ecosystem has its own purpose. From the Captain (Delta Lake) to the Finisher (Delta Merge), I will walk you through how they all come together for a seamless game plan in the world of Data Engineering.

Delta Lake – The Captain of the Team

Traditional Data Lakes are excellent for storing large amounts of data, but they won’t guarantee data consistency and reliability for managing updates. Delta Lake resolved these problems, making it ideal for handling both real-time streaming and batch process data. Traditional Data Lakes – Amazon S3, Azure Data Lake Storage Gen 2.

Use cases in real-time?

Delta Tables – The Opening Batsman

What is Delta Table?

Delta Tables are the core table format on Databricks, built on top of Delta Lake. It combines the structure and querying power of traditional databases with the scalability and flexibility of data lakes, making it easier to store, access, and analyse large amounts of data.

Advantages of Using Delta Tables –

Delta Tables make querying large datasets easy, no matter if you are using SQL or Python. Delta Tables supports MERGE (upsert) and DELETE operations, which is challenging in traditional big data systems. Delta tables can be used for both Batch and Streaming data processing. Delta tables are built on parquet, an open-source columnar storage format.

Alternative tools –

You might think of other table formats like Iceberg for example.

Use cases in real-time?

In Transportation, Delta Tables are the actual data structures where logistics companies store detailed records of shipments, vehicle locations, delivery routes, and timestamps for each transaction.

Delta Live Tables (DLT) – The All-Rounder

What is DLT?

Delta Live Tables (DLT) is a feature in Databricks that easy the process of managing data pipelines, like how Azure Data Factory (ADF) helps automate ETL workflows across multiple data sources. We define the data transformations we want, and DLT automates tasks like scheduling, quality checks, and scaling your operations based on the need, It is helpful especially for real-time or frequently updated data.

Advantages of Using Delta Tables –

Delta Live Tables is useful because it streamlines the process of building and managing data pipelines, reducing the time and effort required to prepare data for analysis. It is ideal if you want to keep your data up to date.

Alternative tools –

I can think of Apache Airflow as pretty close. It is a flexible, open-source platform for orchestrating ETL workflows, but it doesn't handle real-time streaming data as smoothly as DLT. At the same time, DLT does not support as wide a range of transformation languages or custom logic as Apache Airflow does.

Challenges –

• Delta Live Tables (DLT) do not support time travel capabilities.
• DLT is a proprietary feature, it is only available within the Databricks ecosystem and cannot be easily used outside of Databricks.

Use cases in real-time?

In Transportation, logistics companies can use DLT to automate the processing and transformation of real-time shipment data, continuously updating delivery statuses, tracking vehicle locations, and optimizing delivery routes as new data flows in. DLT ensures that data is always up to date, providing real-time insights for timely decisions for efficient delivery operations.

Delta Engine – The Fast Bowler

Delta Engine is the key part of Databricks Delta Lake Ecosystem, which keeps everyone's eyes on the Databricks. Delta Engine is an optimized query engine for speeding up SQL and DataFrame operations on Delta Lake. This delta engine is created to handle large datasets and complex queries more efficient way, so you can get the insights faster and smoothly.  

Advantages of Using Delta Engine –

Delta Engine boosts performance, making it simple to work with huge datasets, whether it is terabytes or petabytes of data. Delta Engine is built to handle complex queries efficiently, so you can perform real-time analytics.

Alternative tools –

In a similar way, Azure Synapse Analytics allows you to query data stored in Azure Data Lake using its SQL engine, although it lacks the deep integration with Apache Spark that Delta Engine offers.

Challenges –

• Limited to Databricks only
• Can be expensive

Use cases in real-time?

You can consider this in all sectors where you are looking for real-time insights.

Delta Sharing – The Team Player 

Delta Sharing is the feature of Databricks to share the data securely across different platforms. It is built on top of the delta lake and lets you share the data across organizations, teams, or platforms in an open, standardized way.

Advantages of Using Delta Engine –

With Delta Sharing, there is no need to physically move or replicate data to share real-time and updated information. Rather than transferring datasets to different systems, other organizations or teams can retrieve the data directly, which makes the sharing process faster and much more efficient, and no vendor lock-in.

Alternative tools –

Snowflake Data Sharing, Google Big Query Data Sharing, and Amazon Redshift Spectrum all offer data sharing, but in my opinion, Snowflake Data Sharing is close as it is secure, real-time, and allows cross-organization sharing.

Use cases in real-time?

In Transportation, Delta Sharing can help logistics companies easily and securely share real-time shipment details, like delivery status and location, with partners such as customs or third-party warehouses.

Delta Transaction Log – The Umpire 

The Delta Transaction Log keeps track of every change we made to data in Delta Lake. With this, your data is always up to date with each operation you perform. ACID compliance and time travel are possible because of DTL.

Advantages of Using DTL –

Delta logs are essential to track each operation we performed in Delta Lake (inserts, updates, and deletes) with time.

Alternative tools –

We can consider Apache Iceberg and Apache Hudi for this.

Use cases in real-time?

Delta Merge – The Finisher 

In Delta Lake, Merge is a useful operation that lets you update or add new data in a single step. It is like saying, If the data already exists, update it. If not, add it. We call it Upserts ( Update + Insert ). This helps to keep your tables up to date. It is often called " The Finisher " because it finalizes updates in a clean and easy way.

Advantages of Using Delta Merge –

Delta Merge simplifies the data updates by allowing inserts, updates, and deletes in one operation, it improves the overall efficiency and ensures data consistency before and after.

Alternative tools –

Apache Iceberg and Apache Hudi both work like Delta Merge, but in my opinion, Apache Hudi is closer.

Use cases in real-time?

In Transportation, Delta Merge can help logistics companies efficiently sync data across multiple systems. For example, when shipment details like delivery addresses, status, or transit time change—Delta Merge can update the system in real-time without any conflicts.

Happy Exploring! Happy Learning!