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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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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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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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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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Snowflake Innovates on Performance & Efficiency While Reducing Costs

Snowflake

In this blog, we will cover some of the most recently launched improvements for the Snowflake platform. For example: Ingest performance: We improved the ingest performance of both JSON and Parquet files with case-insensitive data up to 25%.

BI 52
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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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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

By employing robust data modeling techniques, businesses can unlock the true value of their data lake and transform it into a strategic asset. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Want to learn more about data governance?