flink-kubernetes-operator VS delta

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example, implementing a real-time anomaly detection model in Kafka Streams would require translating Python code into Java, slowing down pipeline performance, and requiring a complex initial setup.

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling.

also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.

Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.

Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg.

Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it.

I think the website is here: https://delta.io

I often hear references to Apache Iceberg and Delta Lake as if they’re two peas in the Open Table Formats pod. Yet…

Here’s the Apache Iceberg table format specification:

https://iceberg.apache.org/spec/

As they like to say in patent law, anyone “skilled in the art” of database systems could use this to build and query Iceberg tables without too much difficulty.

This is nominally the Delta Lake equivalent:

https://github.com/delta-io/delta/blob/master/PROTOCOL.md

I defy anyone to even scope out what level of effort would be required to fully implement the current spec, let alone what would be involved in keeping up to date as this beast evolves.

Frankly, the Delta Lake spec reads like a reverse engineering of whatever implementation tradeoffs Databricks is making as they race to build out a lakehouse for every Fortune 1000 company burned by Hadoop (which is to say, most of them).

My point is that I’ve yet to be convinced that buying into Delta Lake is actually buying into an open ecosystem. Would appreciate any reassurance on this front!

Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.

The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json.

Delta 3.0 extends an olive branch. https://github.com/delta-io/delta/releases/tag/v3.0.0rc1

Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through https://delta.io . They integrate pretty seamlessly to other data visualisation tooling if you want to use it for that but their built in graphs are fine for most cases. They also have ML on rails type through menus and models if I recall but I typically don't use it for that. I've typically used it for ETL or ELT type workflows for data that's too big or isn't stored in a database.

Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).

As the data source, what I have is a gRPC stream. I get data in protobuf encoded format from it. This is a fixed part in the overall system, there is no other way to extract the data. We plan to ingest this data in delta lake, but before we do that there are a few problems.

Take a look at Delta Lake https://delta.io, it enables a lot of database-like actions on files

I prefer parquet (or delta for larger datasets. CSV for very small datasets, or the ones that will be later used/edited in Excel or Googke sheets.