dbt-spark
airbyte
dbt-spark | airbyte | |
---|---|---|
7 | 139 | |
364 | 14,054 | |
1.6% | 2.4% | |
8.6 | 10.0 | |
6 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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dbt-spark
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Trying Delta Lake at home
Spark + dbt => https://github.com/dbt-labs/dbt-spark/blob/main/docker-compose.yml
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So now dbt is worth $4.2b! Yes, that's a "b" for billion.
So the idea is you land your data raw in a Delta bronze layer, and then use dbt models to propagate that data forward to silver, gold, do all of your data quality, etc. and all of the actual execution is happening on a Databricks SQL endpoint (or you can use the dbt-spark adapter and run your transfors as Spark on a cluster)
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Show HN: SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
Neat. Congratulations on the launch!
Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch [0]?
When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.
Some suggestions (for a later date):
1. Add built-in support for Ray [1] (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt [2].
2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).
3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.
If I may, where are you building this product from? And how many are on the team?
Thanks.
[0] https://aws.amazon.com/batch/use-cases/
[1] https://ray.io/
[2] https://getdbt.com/
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Replacing Segment Computed & SQL Traits With dbt & RudderStack Warehouse Actions
It will be helpful to set the stage, as no two technical stacks are the same and not all data warehouse platforms provide the same functionality. It's for the latter that we really like tools like dbt, and the sample files provided here should provide a good starting point for your specific use case. Our instance leverages the cloud version of dbt and connects to our Snowflake data warehouse, where models output tables in a designated dbt schema.
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Your default tool for ETL
T: SQL - views and scheduled queries in BigQuery; planning to go hard with dbt as soon as I can find some breathing room)
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7 Alternatives to Using Segment
Since all of the data is often already in the data warehouse, the logical choice is to simply just use it as a CDP. A modern data stack should consist of an end-to-end flow from data acquisition, collection, and transformation. In most cases, the easiest way to enable this goal is by leveraging tools that are purposely designed to handle a single task. Fivetran, Snowflake, and dbt are great examples of this. In fact, this is the core technology stack that every data-driven company is adopting. Fivetran handles the entire data integration aspect providing a simple SaaS solution that helps businesses quickly move data out of their SaaS tools and into their data warehouse. Snowflake provides an easy way for organizations to consolidate their data into one location for analytics purposes. Lastly, dbt provides a simple transformation tool that is SQL-based, enabling users to create data models that can be reused. These three solutions combined create an effective data management platform.
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Dbt with Databricks and Delta Lake?
This is the issue: https://github.com/dbt-labs/dbt-spark/issues/161. Too bad they still haven't fixed it!
airbyte
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Launch HN: Bracket (YC W22) – Two-Way Sync Between Salesforce and Postgres
I'l also give a shout-out to Airbyte (https://airbyte.com/), with which I've had some limited success with integrating Salesforce to a local database. The particular pull for Airbyte is that we can self-host the open source version, rather than pay Fivetran a significant sum to do this for us.
It's an immature tool, so I don't yet know that I can claim we've spent _less_ than Fivetran on the additional engineering and ops time, but it feels like it has potential to do so once stabilized.
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
- All the ways to capture changes in Postgres
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Airbyte API and Terraform Provider – available in open source
When it says "available in open source", is that under the main airbyte repo's licensing [1], hence primarily licensed under the Elastic License v2 and therefore not typically considered open source by many?
Airbyte has previous of advertising their offering as open source while not really being as per the OSD[2]. This has been raised with them previously but without response [3][4]. They've also been extending their use of ELv2, recently moving many of their existing MIT licensed connectors to be ELv2 [5].
[1] https://github.com/airbytehq/airbyte/blob/master/LICENSE
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Need help moving 16gb of mongodb data to tableau
As possible solution, I can suggest Airbyte(https://airbyte.com/). it's more performant than generic python script.
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Connecting data sources to Xata with Airbyte and Zapier integrations
Airbyte, an open-source data integration engine that offers hundreds of connectors with data warehouses and databases, has gained popularity for its seamless integration and data syncing capabilities. Xata's integration with Airbyte offers a streamlined data ingestion process from any Airbyte input source directly into your Xata database.
- Data replication from postgresql to MSSQL
- Testing
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
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airbyte VS cloudquery - a user suggested alternative
2 projects | 2 Jun 2023
What are some alternatives?
dbt-databricks - A dbt adapter for Databricks.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
rudderstack-docs - Documentation repository for RudderStack - the Customer Data Platform for Developers.
dagster - An orchestration platform for the development, production, and observation of data assets.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
damons-data-lake - All the code related to building my own data lake
meltano
cargo-crates - An easy way to build data extractors in Docker.
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
nimbo - Run compute jobs on AWS as if you were running them locally.
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs