Apache Zeppelin Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more Featuretools An open source framework for automated feature engineering written in python Optimus Cleansing, pre-processing, feature engineering, exploratory data analy...
pythondata-sciencedatamachine-learningsqlsparkpipelineetlpipelinesorchestrationartificial-intelligencedata-engineeringdata-integrationdbtelttransformationdata-pipelinesreverse-etl UpdatedNov 26, 2024 Python Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. ...
Learn how JetBlue streamlined their data transformation process and improved data accuracy with dbt, saving time and increasing productivity.
To effectively utilize growing data assets, IT leaders need a robust data engineering strategy and advanced data platform equipped with two critical capabilities: data loading and data replication. While the benefits of data loading and replication are substantial, they're not always easy to quantify...
Data Engineering with AWS Oct 2023 636 pages 4.8 (31) eBook R$49.99 R$231.99 ADD TO CART Data Modeling with Snowflake May 2023 324 pages 4.9 (18) eBook R$49.99 R$222.99 ADD TO CART Data Engineering with dbt Jun 2023 578 pages 4.9 (8) eBook R$49.99 R$222.99 ADD TO...
From foundational data engineering and data architecture to helping clients create beautiful dashboards, it’s truly end to end. One area we’ve seen explode in recent years is an increased... Data My Favorite Project This Year: The Semantic Layer White Paper Mark Cordeiro and Jack Hulbert ...
Popular tools for analytics engineering include: dbt (data build tool): This is used to transform data in your warehouse using SQL. BigQuery: A fully managed, serverless data warehouse for large-scale data analytics. Postgres: A powerful, open-source relational database system. Metabase: An open...
dbt Labs, a pioneer in analytics engineering, is acquiring SDF Labs, the team of former Meta and Microsoft engineering leaders behind SDF, a next-generation data transformation technology—enabling dbt Labs to integrate SDF's powerful multi-dialect, dbt-native SQL comprehension capabilities into dbt...
6. Analytics Engineering Analytics engineering is also important to learn. It consists of: ETL (Extract Transform and Load) Creating data models (dbt model) Testing and documenting Deployment to the cloud and locally Visualizing the data with analytical application (google data studio and metabase) ...
It may need to pull data from multiple sources, handle prompt engineering and RAG workflows, and interact directly with various tools to execute deterministic workflows. The orchestration required is complex, with dependencies on multiple systems. And if the agent needs to communicate with other ...