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An inspiration engine for ideas
Let us briefly touch on the concepts of data science and data engineering. If we go back to the DIKW triangle, we can say that data science focuses on extracting knowledge and wisdom from the information we have. Data scientists combine tools from mathematics and statistics to analyze information to arrive at insights. Exponentially increasing amou
... See morePierre Pureur • Continuous Architecture in Practice: Software Architecture in the Age of Agility and DevOps (Addison-Wesley Signature Series (Vernon))
(1) The separation between storage and compute , as encouraged by data lake architectures (e.g. the implementation of P would look different in a traditional database like PostgreSQL, or a cloud warehouse like Snowflake). This architecture is the focus of the current system, and it is prevalent in most mid-to-large enterprises (its benefits that be... See more
Jacopo Tagliabue • Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie.
Hex - Do more with data, together.
hex.tech
Koheesio
CI/CD
Package
Meta
Koheesio, named after the Finnish word for cohesion, is a robust Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.
The framework is versatile, aiming to support multiple implementations and working sea... See more
CI/CD
Package
Meta
Koheesio, named after the Finnish word for cohesion, is a robust Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.
The framework is versatile, aiming to support multiple implementations and working sea... See more
GitHub - Nike-Inc/koheesio: Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.
bound for the data warehouse and BI applications. ETL tools are central to first-rate BI environments. They are mature tools that reduce development time, manage the flow of data along the BI value chain, and provide the means to manage changes to data over time as transactional systems and enterprise applications evolve. One key component of an ET
... See moreSteve Williams • The Profit Impact of Business Intelligence
Deep-ML
deep-ml.comETL
The part of the system I'm most proud of, and on which I spent the most effort, is the ETL process.
We had a series of shell scripts for each data source we ingested (there were many), which would pull the data and put it in an s3 bucket.
Then, early in the morning, a cron job would spin up an EC2 instance, which would pull in the latest ETL code... See more
The part of the system I'm most proud of, and on which I spent the most effort, is the ETL process.
We had a series of shell scripts for each data source we ingested (there were many), which would pull the data and put it in an s3 bucket.
Then, early in the morning, a cron job would spin up an EC2 instance, which would pull in the latest ETL code... See more
Bill Mill • notes.billmill.org
Evidence - Business Intelligence as Code
evidence.dev
ETL
The part of the system I'm most proud of, and on which I spent the most effort, is the ETL process.
We had a series of shell scripts for each data source we ingested (there were many), which would pull the data and put it in an s3 bucket.
Then, early in the morning, a cron job would spin up an EC2 instance, which would pull in the latest ETL code... See more
The part of the system I'm most proud of, and on which I spent the most effort, is the ETL process.
We had a series of shell scripts for each data source we ingested (there were many), which would pull the data and put it in an s3 bucket.
Then, early in the morning, a cron job would spin up an EC2 instance, which would pull in the latest ETL code... See more