Delta Lake: Accelerate big data workloads

What is Delta lake? 

Delta lake is an open-source storage layer that brings ACID transactions to Spark and big data workloads on data lakes.

Delta lake will manage the tables and data via Spark API.

Delta lake supports Parquet format, Schema enforcement, time travel, and upserts and deletes operations.

Why delta lake? or What are the present major challenges of Big Data?

most of the company's / client their application data is moving on-premises to cloud and multiple systems are integrated with cloud in data lakes in Big data.

below are the missing features on the Big data Data Lake.

Missing ACID proprieties: Multiple inserts, updates, and deletes we can't do at the same time on Big data Data lake.

Schema enforcement: Whenever source system data types got updates Hadoop big data jobs should support updating the latest change but this is not possible with current big data Bata Lakes. if a new column is added or changed Hadoop application should support this change update this latest information.

lack of consistency and data quality: I can say big data major challenge is data quality presently it does not have constraints and Delta lake is having two constraints: NOT NULL and Check Constraints this will helps for data promising. regarding consistency, if we have huge data with a large number of files while reading multiple folders with hierarchical folders it is a very costly process to fetch the data, if we need to check the small volumes of data also it will check so many files to retrieve the data that will directly impact the performance.

corrupted data rollback : 

we can get frequent job failure because multiple users are inserting maybe storage issues or multiple DML operations on parallel applications using, cardinality issues or corrupted data we can face different scenarios if the corrupted data is preset we can't rollback it directly it bit difficult in the data lake.

Delta Lake will assist to remediate the above challenges.

Delta Lake fetures:

Compatible with Apache Spark API: Developers can use Delta Lake with their existing data pipelines with minimal change as it is fully compatible with Spark, the commonly used big data processing engine.  

Scalable Metadata Handling: Delta Lake treats metadata just like data and adding to spark with distributed processing power to handle all its metadata. so it can handle the petabyte-scale tables with a huge number of partitions and files.

Data versioning (time travel): Delta Lake allows snapshots of data, so developers can retrieve the versions of the object. Using timestamp as well version technic object can be restored.

Parquet Format: All data in Delta Lake are stored in the Apache Parquet format, enabling Delta Lake to leverage the efficient compression and encoding schemes that are native to Parquet. Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.

Unified Batch and Streaming : A table in Delta Lake is both a batch table, as well as a streaming source and sink. Streaming data ingest, batch historic backfill, and interactive queries all just work out of the box.

Schema Enforcement: Delta Lake provides the ability to specify your schema and enforce it. This helps ensure that the data types are correct and required columns are present, preventing bad data from causing data corruption.

Schema Evolution: Big data are continuously changing. Delta Lake enables you to make changes to a table schema that can be applied automatically, without the need for cumbersome DDL.

Merge Statement: Delta Lake supports the merge statment (same as like Oracle merge statments) , if data is present it will update the record else it will insert the record.

Slowly Changing Dimension (SCD): big data world SCD implementation is a pain process so normally redudent data will be copied and sql handles the logics, but Deleta lake enables the DML oprations so we can utilize and implement the SCD changes on top of hadoop data.

Delta Lake architecture:

The next version of Traditional Lambda architecture is Delta lake. Normal practices data pipelines that allow us to combine batch and streaming workflows through a shared filestore with ACID-compliant transactions.

Bronze tables contain raw data ingested from various sources (JSON files, RDBMS data, IoT data, etc.).

Silver tables will provide a more refined view of our data. We can join fields from various bronze tables to enrich streaming records, or update account statuses based on recent activity.

Gold tables provide business-level aggregates often used for reporting and dashboarding. This would include aggregations such as daily active website users, weekly sales per store, or gross revenue per quarter by the department.

The end outputs are actionable insights, dashboards, and reports of business metrics.

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