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?
If
you are in the same Lake, It is easy to feel overwhelmed,
but here is the good news: once you break it down, it is not as complicated as
it seems. In this Blog, I will walk you through each Delta tool, explain
its purpose, and show how it fits in real-world scenarios. I will also share
some alternatives to help you make connections.
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
Delta Lake is the primary piece of the Databricks Delta Ecosystem. It is
an open-source storage layer designed to make a data lake as reliable as a
database by adding the ACID transactions (so data updates are accurate and
safe), data version control (to go back in the history and track changes over
time), and schema enforcement (to keep our data structured).
Why Delta Lake –
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.
If
you think, what is the alternative to Delta Lake, you can think of Apache Iceberg or Apache Hudi, As per me Apache
Iceberg would be the closest because it supports schema evolution and
versioning, but it may lack Delta Lake’s Seamless integration with Spark and
Databricks. Use cases
in real-time?
In Finance,
banks can use Delta Lake for real-time fraud detection by monitoring
transaction patterns, ensuring timely alerts and accurate reporting of
suspicious activities.
In Transportation,
logistics companies rely on Delta Lake to track shipments and optimize delivery
routes, providing up-to-date information for efficient fleet management and
customer satisfaction.
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?
In a Bank’s Transaction System, Delta Transaction Logs can
be used to track every change in customer account balances. For Example, when a
deposit or withdrawal is made, the transaction log captures the change,
ensuring accurate and consistent updates across all systems.
In Transport, for a logistics company, Delta Transaction
Logs can be used to track changes in shipment statuses. When a package is
loaded, in transit, or delivered, the log captures these events, helping to
maintain accurate tracking records.
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!
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KARTIKMOD
