Unveiling Delta Lakes :  Revolutionizing Data Lake Management with ACID Transactions and Scalable Metadata

Why Hive lacked the functionality of update functionality?

•  Hive is a tool that helps you store and process data. One of the things that can be hard to do in Hive is to update data that's already been stored. 
•  Hive is designed to store data in a way that makes it hard to change things once they've been saved. Instead, the usual way to work with data in Hive is to add new data or replace entire pieces of data with updated versions. 
•  This way of working with data is good for processing large amounts of data at once, but it can be slow and difficult to update individual pieces of data.
• When you use Spark to work with data, you can store that data in something called a Hive table. But there's a problem with Hive tables: you can't easily update the data in them once it's been saved. That means if you make a mistake or need to change something, you have to create a whole new table with the corrected data. To fix this problem, a new tool called Delta Lake was created. 
• Delta Lake is like a Hive table, but it lets you update the data in it more easily. That means you can fix mistakes or make changes without having to create a whole new table. Delta Lake is a useful tool for anyone who works with data and needs to make changes to it over time.

What exactly is Delta Lake?

• Delta Lake is a way to store data that's open-source, which means it's free to use. It's designed to work with something called a data lake, which is a big pool of data that you can use to analyze and get insights from. 
• Delta Lake has some special qualities that make it really good at storing data. One of these qualities is called ACID. This means that the data is stored in a way that's really dependable and reliable. 
•  Delta Lake can also handle a lot of data really well. It can work with data that's coming in really fast, like from a live stream, or data that's being processed in batches.
• Delta Lake is designed to work with a tool called Spark. Spark is a tool that's often used for analyzing data. Delta Lake is completely compatible with Spark, which means it's really easy to use. 
•  Delta Lake stores data in a way that's really efficient. It uses something called the Parquet format. This format is really good at compressing data so it doesn't take up too much space. It's also really good at encoding data so it's easy to read and analyze.
• Delta Lake works on top of other data storage systems, like S3, ADLS, GCS, and HDFS. This means that you can use Delta Lake with data that's already stored in these systems, without having to move it to a different location.
  

Why should you utilize Delta Lakes?

• Delta Lake is a tool that helps you store data in a way that's really reliable and dependable. 
• It makes sure that the data is always consistent, so people who read the data will never see anything that's confusing or doesn't make sense. 
• Delta Lake can store metadata and data in the same way. This means that it can handle really big tables with lots of partitions and files without slowing down. 
• Delta Lake can work with data that's coming in really fast, like from a live stream, or data that's being processed in batches. 
• It helps you make sure that the data is always in the right format. This means that it's easy to read and analyze. 
• Delta Lake keeps track of every change that's made to the data. This means that you can always see what's happened to the data in the past. 
• Delta Lake is really easy to use with a tool called Spark. This means that you can use it with other tools you might already be using to analyze data.

Delta Lakes vs Parquet:

Although it is a wrapper over the Parquet data format, Delta Lake also provides some extra capabilities. Here are the distinctions:

• Delta Lake is an open-source storage layer that addresses the limitations of traditional data lakes in Hadoop architecture.
• It adds ACID compatibility and scalable metadata management, ensuring data lake dependability and enabling ACID transactions on Spark.
• Delta Lake operates on top of existing data lakes, supporting Parquet format and providing schema enforcement, time travel, and inserts/deletes.
• It treats metadata as data, managing petabyte-scale tables without the Small file problem.
• Delta Lake serves as a batch table and streaming source/sink.
• It is compatible with Spark, allowing easy integration with existing data pipelines.
• Overall, Delta Lake offers a reliable and efficient solution for data lake management, improving data consistency, scalability, and transactional capabilities.