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Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

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A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

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Smart Schema: Enabling SQL Queries on Semi-Structured Data

Rockset

In this blog post, we show how Rockset’s Smart Schema feature lets developers use real-time SQL queries to extract meaningful insights from raw semi-structured data ingested without a predefined schema. In NoSQL systems, data is strongly typed but dynamically so.

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Data Warehouse vs Big Data

Knowledge Hut

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Data warehousing offers several advantages.

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Data Vault on Snowflake: Feature Engineering and Business Vault

Snowflake

Data Vault as a practice does not stipulate how you transform your data, only that you follow the same standards to populate business vault link and satellite tables as you would to populate raw vault link and satellite tables. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

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DataOps vs. MLOps: Similarities, Differences, and How to Choose

Databand.ai

An enterprise looking to streamline its entire end-to-end analytics lifecycle may implement a comprehensive solution incorporating best practices from each approach—starting with robust data ingestion (DataOps) through optimized model training and deployment (MLOps). Better data observability equals better data quality.

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Data Engineering Weekly #108

Data Engineering Weekly

With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation. The blog narrates the European Commission’s updated version of the European Standard Contractual Clauses (EU SCCS) and how to prepare to handle the privacy laws. Kudos to the author and the Atlassian team.