exagolo

4 ms·

1.
Why Your BI Stack Knows More About Your Processes Than You Think(exasol.com)
11 points by exagolo 8mo ago|0 comments
2.
Exasol Virtual Schemas: The Key to Flexible Deployments(exasol.com)
2 points by exagolo 9mo ago|0 comments
3.
From Data Federation to AI-Ready Analytics with Virtual Schemas(exasol.com)
14 points by exagolo 9mo ago|0 comments
4.
by exagolo 9mo ago
I believe Karma solely comes from upvotes for comments minus downvotes. Submissions don't count.
5.
by exagolo 10mo ago
In case you have a single table with time-series data, then Clickhouse will perform typically better. It's very much optimized for this type of use cases. Once you are joining tables and having more advanced analytics, than Exasol wil
6.
by exagolo 10mo ago
I think the issue in the tests was the lack of a proper resource management of Clickhouse that led to queries failing under pressure. Although I have to admit that the level of pressure was minimal. Just a few concurrent users shouldn'
7.
by exagolo 10mo ago
You mean the "execution plan" for your queries? Ideally, those types of decisions are automatically done by the database.
8.
Database Benchmarks Lie (If You Let Them)(exasol.com)
15 points by exagolo 10mo ago|12 comments
9.
by exagolo 10mo ago
Traditional database benchmarks focus on throughput and latency – how many queries per second can be processed, how execution time changes as hardware resources increase. This benchmark revealed something different: reliability under realis
10.
by exagolo 10mo ago
The tool is very flexible and you can create your own benchmarks with your own data. This is always the best benchmark, as any public benchmark will have a certain bias.
11.
A new open-source benchkit to create your own reproducible database benchmarks(exasol.com)
11 points by exagolo 10mo ago|3 comments
12.
by exagolo 10mo ago
Another alternative is Exasol that is factors (>10x) faster than Clickhouse and scales much better for complex analytics workloads that joins data. There is a free edition for personal use without data limit that can run on any number of
13.
by exagolo 10mo ago
For the bigger tasks, Exasol might also be a very neat option for you. We have a free personal edition that can scale regarding data volumes, #servers (MPP architecture) and complex workloads. Recently, we have also compared ourselves again
14.
by exagolo 11mo ago
If not having to adjust queries is a major driver for your considerations, then I would highly recommend looking at SQLGlot ( https://github.com/tobymao/sqlglot ), a transpiler that makes you (more) independent of query
15.
by exagolo 1y ago
Are you using a transpiler technology such as SQLGlot for the multi-vendor SQL generation?
16.
by exagolo 1y ago
I do agree. I still think that the article articulates a very interesting thought... the better the input for a problem, the better the output. This applies both to LLMs but also for colleagues.
17.
by exagolo 1y ago
When people complain about large language models, I often feel like they're complaining about their coworkers without realizing it...
18.
All Your Coworkers Are Probabilistic Too(scatterarrow.com)
5 points by exagolo 1y ago|9 comments
19.
by exagolo 1y ago
Agreed, and it's an amazingly well-maintained GitHub repo: https://github.com/tobymao/sqlglot Big kudos to Toby and the team.
20.
by exagolo 1y ago
I do agree. SQL is simply an access API for so many systems, and nice as it's a declarative language rather than a normal programming language. LLMs are super powerful to express questions to data that can then be translated into SQL.
21.
by exagolo 1y ago
For translation between dialects, you could use projects such as SQLGlot. The advantage of SQL is the standardization over many decades (yes, I know that it's still a mess with the different dialects).
22.
Text-to-SQL is dead, long live text-to-SQL(exasol.com)
62 points by exagolo 1y ago|49 comments
23.
MariaDB and Exasol announce strategic partnership for high-performance analytics(mariadb.com)
14 points by exagolo 1y ago|2 comments
24.
by exagolo 1y ago
MariaDB Exa is engineered for extreme scale, complex queries and high performance on multi-terabyte datasets. This approach ensures every MariaDB customer has the right analytical solution to maximize performance and efficiency across their