Time travel is a quite popular Delta Lake feature. But do you know it's not the single one you can use to interact with the past versions? An alternative is the RESTORE command, and it'll be the topic of this blog post.
One of the big challenges in streaming Delta Lake is the inability to handle in-place changes, like updates, deletes, or merges. There is good news, though. With a little bit of effort on your data provider's side, you can process a Delta Lake table as you would process Apache Kafka topics, hence without in-place changes.
Streaming Delta tables is slightly different from streaming native streaming sources, such as Apache Kafka topics. One of the significant differences is schema enforcement. It leads to the job failure in case of schema changes of the streamed table.
The previous blog from the series we discovered streaming reader. However, an end-to-end streaming Delta Lake pipeline also requires a writer which will be our focus today.
Even though I'm into streaming these days, I haven't really covered streaming in Delta Lake yet. I only slightly blogged about Change Data Feed but completely missed the fundamentals. Hopefully, this and next blog posts will change this!
Checkpoints are a well-known fault-tolerance mechanism in stream processing. But what does it have to do with Delta Lake?
If you have some experience with RDBMS, who doesn't btw, you have probably run a VACUUM command to reclaim the storage space occupied by deleted or obsolete rows. If you're now working with Delta Lake, you can do the same!
If Delta Lake implemented the commits only, I could stop exploring this transactional part after the previous article. But as for RDBMS, Delta Lake implements other ACID-related concepts. One of these are isolation levels.
One of the great features of modern table file formats is the ability to handle write conflicts. It wouldn't be possible without commits that are the topic of this new blog post.
Data lakes have made the data-on-read schema popular. Things seem to change with the new open table file formats, like Delta Lake or Apache Iceberg. Why? Let's try to understand that by analyzing their schema evolution parts.
In my recent exploration of the compaction, aka OPTIMIZE command, in Delta Lake, I found this famous Z-Ordering mode. It was one of the most outstanding features when I first heard about Delta Lake. You can't even imagine how impatient I was to see what it is doing under-the-hood. Fortunately, this time has come!
I like writing code and each time there is a data processing job to write with some business logic I'm very happy. However, with time I've learned to appreciate the Open Source contributions enhancing my daily work. Mack library, the topic of this blog post, is one of those projects discovered recently.
The small files is a well known problem in data systems. Fortunately, modern table file formats have built-in features to address it. In the next series we'll see how.
It's time to start the 4th part of the Table file formats series. This time the topic will be Change Data Capture, so how to stream all changes made on the table. As for the 3rd part, I'm going to start with Delta Lake.
In the previous blog post about Delta Lake you discovered the logic for the writing part. Meantime Delta Lake 2 was released and it's for this brand new version that I'm going to share with you some findings related to the data reading.
It's time for the last data generation part of the ACID file formats series. This time we'll see how Delta Lake writes new files.
All in one! Ebook with data engineering design patterns on AWS, Azure, and GCP
