delta VS Redash

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.

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.

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.

This sounds like a new trending destination to take selfies in front of, but it’s even better than that. Delta Lake is an “open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance.” (source). It let’s you interact with an object storage system like you would with a database.

I mean the different between using the delta.io framework to let it run on your own machines/ vms vs using databricks and have clusters defined.

You are right, delta.io is just a framework. Sorry for the unclear question. Another try: when you host spark on your own with delta as table format compared to usage of Databricks, what are the differences?

Yeah, a lot of the time I'd agree with you.

This container came about for the Redash project (https://github.com/getredash/redash), which has been stuck on PostgreSQL 9.5 (!) for years.

Moving to a new PostgreSQL container version is easy enough for new installations, but rolling that kind of change out to an existing userbase isn't so pretty.

For people familiar with the command line, PostgreSQL, and Docker then no worries.

But a large number of Redash deployments seem to have been done by people not skilled in those things. "We deployed it from the Digital Ocean droplet / AWS image / etc!"

For those situations, something that takes care of the database upgrade process automatically is the better approach. :)

I have tried Metabase, Redash beore (both self hosted open source versions), from my experience I find Metabase a bit easy to work with.

Regarding visualization tools, sqliteviz has proven to be the best I've found so far. Their web app runs locally but has some trackers, so I run it locally via a simple, static HTTP server. Falcon and Redash seem like overkill for my needs.

Redash seems to be dead or at least in hibernation. There hasn't been a release in over a year.

https://github.com/getredash/redash/issues/5891

redash

In addition to metabase there are redash[0] and apache superset[1]. They are more or less similar to metabase with some different quirks. You can also visualize quite a bit of data in grafana[2] as well.

[0] https://redash.io/

[1] https://superset.apache.org/

[2] https://github.com/grafana/grafana

Source: https://github.com/getredash/redash/graphs/contributors