“People Still Struggle to Get Value Out of Their Existing Data Lake. This Data Journey Happens Over and Over Again in Organizations Because Data is Often Messy in the Real World”.
Organizations have many data. It can be curated customer data in your OLTP system, raw clickstreams from your web server, or unstructured data from different sensors.
Data Lake promises that you can dump all the data in the Data Lake, which is powerful when comparing it with a traditional database. In a traditional database, you have to start coming up with a schema while doing much cleaning.
However, the problem here is it has become really complicated and that you’re wasting much money and time-solving system problems rather than what you really want to be doing, which is extracting value from your data.
And the way we look at it is all distractions of the Data Lake that prevents you from actually accomplishing your job at hand.
So, here’s five ways how Delta Lake overcomes the challenges of Data Lakes.
#1: It adds ACID transactions to your Data Lake
An ACID transaction makes the operations really reliable to have reliable operations on your Data Lake.
#2: Schema Enforcement
When the data flows in from the Data Lake into Delta, it makes sure that your data is a high-quality one with the right schema. If there is an issue with the schema, it’ll be put back into the Data Lake and wait for it until it is corrected before it makes its way into Delta Lake, which is considered a significant factor in the Delta Lake process. It always gives you a high quality so that the use cases that you want to do later on actually work on the unification of data in AI.
#3: Mixing Streaming & Batch
The reliable transaction is actually enabled for the first time to mix batch and streaming in a way that hasn’t been done before.
In particular, to give you an example, you could have one table with data in it.
You could have streaming data being appended written into the same table. While simultaneously, you have multiple readers and writers who are concurrently accessing that data. While they are doing that using Delta Lake, you won’t find any corruption or consistency issues.
#4: Scalable Metadata Handling
Enterprises collect many data into the Data Lake, and the data tends to become pretty large. Sometimes it leads to many terabytes or even larger than that creates trouble letting the systems hard to manage.
Delta Lake figures out how to basically scale through Apache Spark and allows the metadata to work super-fast.
#5: Time Travel- The Killer Feature of Delta Lake
Delta looks at all the data when coming in and all the metadata stored and lineage for your data. It also integrates well with the Machine Learning Flow, allowing you to leverage Delta’s time travel to reproduce any model that you had created in the past, bringing a true unification of Machine Learning and data.
Delta Lake gives five benefits to overcoming the barriers of Data Lake.
It prevents data corruption.
Queries are faster.
It increases data freshness.
It reproduces the ML models.
It helps to achieve compliance easily.
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