Why Business Needs Delta Lake in 2021?
A delta lake is a non-proprietary storage layer that brings authenticity to data lakes. Delta Lake delivers ACID transactions, ascendable metadata handling, and unification of batch and streaming data development. Delta lake also runs with the contemporary data lake and is in full harmony with Apache Spark Application Programming Interfaces.
History: Originally Developed by Databricks as a project, Delta Lake is now free with the Linux Foundation project.
Why business needs Delta Lake?
By applying a Delta Lake, the main advantages your business can have:
The issue with current Data Architectures
To develop, and run, and maintaining current data architectures is a problem. Primarily present data architectures use a blend of at least 3 diverse systems:
Business data comes via streaming networks; for instance, Amazon Kinesis, which mostly focuses on speedy delivery. It is then assembled in data lakes (like Amazon S3), which are optimized for extensive, cost-effective storage.
Data lakes lack the performance and aspects needed to support top-level business applications.
Hence, the most critical data is synced to data warehouses, enhanced for remarkable performance and protection.
The architecture of the data lake
Earlier, Lambda architecture was quite popular. It is a technique in which records are worked upon by a batch system and parallelly in a streaming system. Only during the query time, the results are merged to give a complete answer.
The downside of lambda architecture is the high cost of growing, running, and administering 2 different systems.
Over the years, there have been several attempts to unify streaming and batch into a sole unique system. But the attempts weren’t successful.
With the arrival of Delta Lake, customers now can take on a basic continuous data flow framework to operate on data as it comes about. This architecture is called The Delta Architecture.
Delta Lake offers the following benefits
In a normal Data lake, at a time, many users would be accessing the data, and it becomes necessary to safeguard the data integrity. Users never see inconsistent data with delta lake because it is continuously isolated to model the data consistent crosswise several users. A critical feature in the volume of the databases, ACID is quite stable than HDFS.
A transaction log is maintained in the delta lake to track all the edits done in the record directory to implement ACID transactions.
Ascendable metadata handling
Leveraging Spark’s dispersed processing power, Delta Lake efficiently manages all the batch files for the petabyte range of records.
Streaming and batch unification
In a Data lake, both batch and stream processing are introduced in a consolidated spot, while in Delta Lake, a record is in the form of a bunch and a streaming launch and sink.
Also, features like Ingesting Streaming data, interactive queries, backfill of back records all work out of the case for delta lake.
The enforcement of the ability to decide and execute the schemaDelta lake helps evade detrimental data to enter the data lakes. It helps in controlling data corruption.
Use Delta Lake for data versioning and get rollbacks, whole past audit series, and reproducible AI practices.
Upsert and delete operation
The Delta Lake architecture aids in functions like delete, merge, and update to empower hard use cases such as change-data-capture and streaming upserts, and so on.
The data construct grows over time as the issues and requirements of a business. By implementing Delta Lake, integrating new dimensions with the asynchronous change in data becomes easy as pie.
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