cube.js
cockroach
cube.js | cockroach | |
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86 | 100 | |
17,174 | 29,119 | |
0.8% | 0.8% | |
9.9 | 10.0 | |
3 days ago | 3 days ago | |
Rust | Go | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cube.js
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MQL – Client and Server to query your DB in natural language
I should have clarified. There's a large number of apps that are:
1. taking info strictly from SQL (e.g. information_schema, query history)
2. taking a user input / question
3. writing SQL to answer that question
An app like this is what I call "text-to-sql". Totally agree a better system would pull in additional documentation (which is what we're doing), but I'd no longer consider it "text-to-sql". In our case, we're not even directly writing SQL, but rather generating semantic layer queries (i.e. https://cube.dev/).
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Show HN: Spice.ai – materialize, accelerate, and query SQL data from any source
I'm not too familiar with https://cube.dev/ - but my initial impression is they are focused more on providing APIs backed by SQL. They have a SQL API that emulates the PostgreSQL wire protocol, whereas Spice implements Arrow and Flight SQL natively. Their pre-aggregations are a similar concept to Spice's data accelerators. It also looks like they have their own query language, whereas Spice is native SQL as well.
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Show HN: Delphi – Build customer-facing AI data apps (that work)
Hey HN!
Over the past year, my co-founder David and I have been building Delphi to let developers create amazing customer-facing AI experiences on top of their data. We're excited to share it with you.
David and I have spent our careers leading data and engineering teams. After ChatGPT got popular, we saw a rush of "chat with your data" startups launch. Most of these are "text-to-SQL" and use an LLM like GPT-4 to generate SQL queries that run directly against a data warehouse or database.
However, the general perception now is most of them make for nice demos but are hard to make work in the real world. The reason is data complexity. Even smart LLMs find it difficult to reason about messy databases with hundreds of tables, thousands of columns, and complex schemas that have been built up piece-meal for years. Text-to-SQL can be a fine dev tool for data scientists and analysts, but we've seen many organizations hesitate to deploy it to end users, who never know if the answer they get one day will be the same the next.
David and I found a better way. From our time in the data engineering world, we were familiar with a type of tool called "semantic layers." Think of them like an ORM for analytics. Basically, they sit between databases (or data warehouses) and data consumers (data viz tools like Tableau or APIs) and map real-world concepts (entities like "customers" and metrics like "sales") to database tables and calculations.
Semantic layers are often used for "embedded analytics" (e.g. when you're building customer-facing dashboards into your application) but are increasingly also used for traditional business intelligence. Cube (https://cube.dev) is a prominent example, and dbt has also recently released one. They're useful because with a semantic layer, the consumer doesn't have to think about questions like "how do we define revenue?" when running a query. They just get consistent, governed data definitions across their business.
We realized that semantic layers could be just as useful for LLMs as for humans. After all, LLMs are built on natural language, so a system that deterministically translates natural language concepts into code has obvious power when you're working with LLMs. With a semantic layer, we've found that companies can get AI to answer much more complex questions than without it.
For a year now, we've been building Delphi to do just that. We've gone through a few iterations/pivots (initially we were focused on building a Slack bot for internal analytics) and are now seeing our developer-first approach resonate. We're being used to power customer-facing fintech applications, recruiting software, and more.
How do you use Delphi? The first step is connecting your database; then, we build your semantic layer on top of it. Right now we do this manually, but we're moving more and more of it over to AI. Once that's done, we have 3 main ways of using Delphi: 1) white-labeling our AI analytics platform and providing it to your customers; 2) a streaming REST API and SDKs; and 3) React components to easily drop a "chat with your data" experience into your app.
If this is interesting to you, drop us a line at [email protected] or sign up at our website (https://delphihq.com) to get in touch. Thanks for reading! Would love to hear any thoughts and feedback.
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Apache Superset
We use https://cube.dev/ as intermediate layer between data warehouse database and Superset (and other "terminal" apps for BI like report generators). You define your schema (metrics, dimensions, joins, calculated metrics etc) in cube and then access them by any tool that can connect to SQL db
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Need to reduce costs - which service to use?
also check out cube.dev. they can do the semantic layer and cache it so you are not hitting Snowflake all the time.
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Anyone with experience moving to Cube.dev + Metabase/Superset from Looker ?
We need metrics to live in source control with reviews. Metabase doesn't have a git integration for metrics, which is why we are convinced to use cube.dev as a semantic layer.
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GigaOm Sonar Report Reviews Semantic Layer and Metric Store Vendors
https://github.com/cube-js/cube comes out very well at the end as a promising open source system, getting rather close to the bullseye. Would love to know more & hear people's experience with it.
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Show HN: VulcanSQL – Serve high-concurrency, low-latency API from OLAP
How is this different from something like https://cube.dev/
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Best Headless Chart Library?
Have a look to cube.js
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Advice / Questions on Modern Data Stack
For now, I've been thinking on using self-hosted Rudderstack both for ingestion and reverse ETL, cube.dev as the abstraction later for building webapps and providing catching for the BI layer, and dbt for transformations. But I have doubts with the following elements:
cockroach
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11 Planetscale alternatives with free tiers
CockroachDB is an open source distributed SQL database designed for scalability and resilience. While it offers SQL databases, CockroachDB is also compatible with PostgreSQL.
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A MySQL compatible database engine written in pure Go
cockroachdb might be close: https://github.com/cockroachdb/cockroach
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No More Free Tier on PlanetScale, Here Are Free Alternatives
CockroachDB - SQL
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Is it bad to create a publicly accessible RDS database for my serverless web app?
For example, when you create a serverless postgres database with a platform like CockroachDB or Neon, you effectively get a connection string with a strong password. Anyone can connect to your database from anywhere so long as they have the right connection string. There are no security settings in these services to change this behavior.
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Linux surpasses the Mac among Steam gamers
> Yes you can on the android emulator. The biggest issue is compu arch in that case.
I can also download VirtualBox and run all Windows programs, that would mean that all Windows apps are Linux apps?
> Yes you can for the most part
You can't statically link glibc: https://github.com/cockroachdb/cockroach/issues/3392
glibc can break stuff: https://www.gamingonlinux.com/2022/08/valve-dev-understandab...
I had binaries break because the newer version if openssl was put under a slightly different name.
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How do small SaaS's handle databases?
Also, worth noting, if you're already using PostgreSQL (or plan to) you might want to take a look at https://www.cockroachlabs.com/ they have a free tier too and CockroachDB has a PostgreSQL interface.
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Go Dependency management in large company projects - How do you do it?
I know that some projects like cockroach use custom build tools like bazel. But we actually really like to use to be able to build our projects simply with the great go toolchain and don't really aim to dive deep into custom build solutions.
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Eli5: Why do companies use the products of Oracle to store information, when they can just use spreadsheets like Excel, or make their own spreadsheet software?
CockroachDB is designed to be globally distributed. It has to handle causality when resolving collisions. It has to account for having a write operation to arrive after another and still have time priority because it was sent out a few milliseconds earlier.
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rage - a minimalistic load testing tool
Cockroachdb created a go runtime patch which measures the Grunning time of a goroutine: https://github.com/cockroachdb/cockroach/pull/82356. It doesn't entirely solve the problem though.
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Data Engineering Tools in Go
Our entire backend is written in Go. We've built a platform that allows other companies to offer automatic data syncing to their customers' data warehouses. Go works great for building distributed systems like this (see K8s). We're not the only ones in the space building data intensive applications with Go. Pachyderm, Pinecone, Cockroach Labs and are all also doing it. We've been quite happy with how Go has worked for us.
What are some alternatives?
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
neon - Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, branching, and bottomless storage.
Druid - Apache Druid: a high performance real-time analytics database.
tidb - TiDB is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics. Try AI-powered Chat2Query free at : https://tidbcloud.com/free-trial
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
yugabyte-db - YugabyteDB - the cloud native distributed SQL database for mission-critical applications.
metriql - The metrics layer for your data. Join us at https://metriql.com/slack
InfluxDB - Scalable datastore for metrics, events, and real-time analytics