What is Delta Lake In Databricks?

Here you will learn an interesting feature called Delta Lake, But first, let’s learn What is delta lake In Databricks? And why do we need it?

If you learn about data lake you must know why we have been using Data Lake, not just to consume the data, but also to store the process data.

So Data Lake is a central repository that can store all types of data, be it structured data or unstructured data, in its native format.

And you can store vast amounts of data in a data lake. And with the growing data volumes, data lakes are becoming increasingly important.

What is Normal Data Lake?

So, what is Data Lake? And why it is important for us? Let us now try to explain the concept with an example.

The data lake is a placeholder for massive amounts of unstructured and semi-structured data.

It contains all types of data, such as structured, semi-structured, or unstructured data and it doesn’t matter if it is big or small, if it is high quality or low quality, etc.

A data lake is a centralized storage that can store all kinds of data, like structured, semi-structured, or unstructured data, in its normal format. It aids in making the data available when needed.

Example of Data Lake:

Data lakes were initially used by the oil and gas industry to consolidate and manage their disparate data sources containing industrial data.

Today, with the growth of Big Data, data science, analytics, and computing-data lakes have become an important part of an organization’s cloud strategies.

This article is all about delta lake which is defined as a replica database in Big Data architecture that stores updated data in a more efficient manner.

Challenges in Normal Data Lake

1. Data Read and Write Incompatibility

First, if multiple processes are trying to read and write to the same file at the same time, there are chances that readers would see dirty data since data writing is not complete yet.

2. Data inconsistency

Next, a very common problem is data inconsistency. If a job fails while writing data, the data is left in a partial state. You have to work around ensuring that data is only written if the job succeeds.

3. Slowing the Performance

Then, as you start appending the data, the data is scattered across an increasing number of files, thus slowing down the performance over a period.

4. Update Records in a File

And finally, the biggest of all is the update of records in a file. Even for a single record update, you may have to read an entire dataset, update the data, and save it back.

5. Slowly Changing Dimensions

This is where typical data warehousing tasks, like slowly changing dimensions, become a challenge. And this is where Delta Lake steps in and was earlier called Databricks Delta.

6. Manual Process is Hard

The obstacles of getting data in and out of a data lake are too much to handle when done manually.

That is why we need to focus on automating workflows using bots that are able to be executed through the use of APIs and taking advantage of a machine learning pipeline.

What is Delta Lake in Databricks?

It’s also a much simpler solution compared to Hadoop or Spark. Delta Lake is an enterprise-grade distributed computing system for storing and managing unstructured data.

Delta Lake is a SQL engine for non-structured data, built on Spark. That makes it an ideal replacement for Hadoop’s HBase, and other key-value stores.

Delta Lake is the successor to Apache Hadoop. Just as with other next-generation data storage solutions, this is an alternative to traditional ways of storing data, so there are many benefits to using delta lake in Databricks.

Benefits of Delta Lake in Data Bricks

Delta Lake in Databricks is a distributed system for storing data and serving data that anyone can build using open source technologies.

Delta Lake allows you to organize the data storage between storage accounts and clusters, where the clusters are comprised of Azure virtual machines of one or more sizes.

How to Work on Delta Lake?

To work with Delta Lake, first, you need to save the data in delta format. To save the DataFrame, like our sampleDF, use the write property, and specify the format as delta.

Then provide a location to save this, As simple as that. This is similar to saving data in other formats, but now the file is in delta format.

Let’s see what happens behind the scenes. Delta Lake is a component that is deployed on the cluster In Databricks, and it is deployed by default via newer versions of Databricks Runtime.

If you are saving the DataFrame in delta format, it gets stored on the storage in Parquet format only, But along with the file, delta stores the transaction log as well.

Feature of Delta Lake

1. ACID transactions

So transaction log is the magic behind the scenes that allows Delta Lake to perform ACID transactions on files, and it uses serializable isolation levels.

2. log information

Just to reiterate, the underlying file format for Delta is nothing but Parquet, and the log information is stored in the _delta_log folder.

3. Audit Details

3. Data in the Delta table is only considered written when the transaction log is updated, otherwise not. And this log also provides the full audit trail of all the changes that have happened.

4. DML Operation

And that’s how it helps in doing MERGE, UPDATE, and DELETE operations on the underlying files.

5. Enforces the Schema

Not just that, but Delta Lake has some other great features too. It enforces the schema on the Delta table that helps in preventing any bad data from being added to the table.

6. Time Traveling

Because of the transaction log, you can get snapshots of older versions of the data from the Delta table.

You can access it by a version number or snapshot at a particular instance of time. This is called time traveling.

7. Z-Ordering

Another great feature is Z-Ordering where you can sort the data in the files based on multiple columns. This allows for quick retrieval of the data.

8. Delta table statistics

By default, the Delta table also collects statistics in form of a minimum and maximum values of each column for each part file.

This is making it very close to the SQL environment. This speeds up query performance as part files can be completely skipped by just looking at the statistics.

9. Garbage Collection

And finally, you might be wondering, what about unused files? Delta Lake allows you to do garbage collection by using the VACUUM command.

This is just a glimpse of what Delta Lake can do. It’s very new, and a lot of features are being added to it. Now that you know that Delta Lake allows you to update the data.

What is this transaction log In Delta Lake?

The transaction log is also called a Delta log, and it’s an ordered log of every single transaction that has been performed on the Delta table since it was created.

So if you want to read the data from the Delta table, the transaction log will be checked, and it will give you the latest view of the table.

The transaction log contains all the commit information of transactions. It contains the operation that has been performed, whether it’s an insert, update, delete, schema change, et cetera, predicates that have been used to update or delete the records, and all the partition files that have been affected because of the operation.

How to Implement Code in Delta Lake?

Let’s write it in SQL. You can use the MERGE statement and specify the target table, which in this case is DimTaxiZones.

Specify the source table, which contains the updates. Here it is StagingTaxiZones. And join both the tables on their keys.

If a record only exists in the target table, then update the values you want. And if it does not exist in the target, you can insert the record. And of course, you can also do deletes here based on requirements.

And you can do this in Scala as well. Easy, right? But let me share a warning. Reading underlying delta files directly may cause issues, and this is because, you know, the delta table stores multiple versions of records.

So to use the file directly, you’ll need to export it in a different format like Parquet or CSV.

Conclusion

So you have seen how useful Delta Lake is, and it can help you build your data warehouse in the data lake reliably.

You can implement slowly changing dimensions in the lake and It also helps to enable change data-capture scenarios.

Using the time travel feature you can restore the data back in case of failures and Delta Lake can also act as common storage for batch and streaming data.

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