dbt-databricks VS sayn

Our current data infra looks a little something like this: 1. Airbyte deployed on EKS for supported data connectors. I’m using the alpha Databricks connector to load directly into Unity Catalog. 1a. S3 bucket for raw landing zone storage if we cannot directly load into Databricks Managed Tables. 2. Orchestration, storage, and transformations are in Databricks. Calling out to the Airbyte api in the EKS cluster to keep all orchestrations inside Databricks. 2a. databricks-dbt for transformations & cleaning.

dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA)

Read this https://github.com/databricks/dbt-databricks

Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose.

Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake.

databricks

Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward.

Data Processing: SAYN

We believe simplicity to be crucial when maintaining pipelines at scale. However, we also believe that simplicity should not come at the expense of flexibility. This is why we have built our own open source data processing framework: SAYN. SAYN is designed to empower analytics teams by being simple, flexible and centralised. It democratises the contribution to data processes within an analytics team, enables full flexibility and helps save a lot of time through automation.