dbt-databricks
Apache Spark
dbt-databricks | Apache Spark | |
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15 | 101 | |
180 | 38,378 | |
1.7% | 0.6% | |
9.5 | 10.0 | |
15 days ago | 7 days ago | |
Python | Scala | |
Apache License 2.0 | Apache License 2.0 |
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dbt-databricks
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Curious if anyone has adopted a stack to do raw data ingestion in Databricks?
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.
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dolly-v2-12b
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)
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Any suggestions for building DBT project on DataBricks?
Read this https://github.com/databricks/dbt-databricks
- dummy
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Clickstream data analysis with Databricks and Redpanda
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.
- Next step for my career..
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DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
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.
- Would you use dbt with databricks? If so, why?
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Welcome, DataEngHack online!
databricks
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A Quick Start to Databricks on AWS
Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward.
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
dbt-spark - dbt-spark contains all of the code enabling dbt to work with Apache Spark and Databricks
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Neo4j - Graphs for Everyone
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
Scalding - A Scala API for Cascading
sql_to_ibis - A Python package that parses sql and converts it to ibis expressions
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
nutter - Testing framework for Databricks notebooks
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.