As data grows with time the complexity to process the same grow together. Organizations nowadays mix up a data warehouse, streaming, and data lake to build big data systems which augment the cost and the complexity to maintain. So, in this article, we will discuss how Databricks Delta solves these problems by providing reliable, high-performance, and simple data pipeline solutions.
Challenges With The Current Data Architecture
Data arrives from streaming sources such as Apache Kafka and stored for long-run in data lakes such as S3. Reading and writing into data lakes are not reliable with poor performance as one of the disadvantage. Thus, valuable data for reliability and high performance is kept in data warehouses at a much higher storage cost than data lakes.
- Also, the application suffers if jobs miss some data or upload error-prone data due to the Extract Transform and Load (ETL) process between these storage systems which can lead to failure of the downstream application.
- It may take several hours to get the data reflected in the data warehouse that is, ETL processes can add latency.
- It is difficult to build flexible data engineering pipelines that combine streaming and batch analytics.
Delta To The Rescue
Databricks delta comes with the combination of reliability and performance of data warehouse, scalability of the data lake, and low latency of streaming ie. unifies into a single management tool.
“Combining the best of data warehouses, data lakes and streaming”
Delta stores data in Apache Parquet format and runs over Amazon S3 to reliably upload, update and, query massive data with low cost. Organizations don’t need to spend their resources in moving data across systems.
Databricks delta augments S3 with extensions such as:
- ACID transactions.
- Automatic data indexing.
These extensions provide a wide variety of optimizations using Delta, while still providing reliable data access for applications to the user.
Who should avail Delta?
Databricks Delta could be the right platform for organizations –
- That is currently using the Hadoop/Spark stack and would like to simplify their data pipeline architecture while improving performance.
- That require high volume processing capabilities but without the hassle of managing the metadata, backups, upserts, and data consistency.
Conclusion
As data grows managing big data applications is important. With Databricks Delta, Instead of adding new storage systems and data management steps. Delta lets organizations remove complexity by getting the benefits of multiple storage systems in one. I hope the points that we discussed in this article can help you to make the right decision on whether Databricks Delta is the right choice for you or not.
In the next article, we will discuss Delta Architecture, till then Stay tuned!! 🙂
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