Remove Data Analyst Remove Data Engineering Remove Data Observability
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DataOps

Dataconomy

Leadership often includes a Chief Data Scientist or Chief Analytics Officer who guides the strategy and vision. Collaboratively, team members such as: Data engineers: Who design and maintain data pipelines. Data analysts: Who interpret data and generate insights.

DataOps 91
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Data Observability Tools and Its Key Applications

Pickl AI

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. For instance, via lineage, analysts can understand if upstream data dependencies have reliable data quality. “At

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Five benefits of a data catalog

IBM Journey to AI blog

It seamlessly integrates with IBM’s data integration, data observability, and data virtualization products as well as with other IBM technologies that analysts and data scientists use to create business intelligence reports, conduct analyses and build AI models.

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The Rise of Open-Source Data Catalogs: A New Opportunity For Implementing Data Mesh

ODSC - Open Data Science

Understanding data mesh Data mesh is a decentralized architecture type that allows different departments to access data independently. It’s different from traditional data architecture, which usually has dedicated data engineering teams that provide access to information after other departments request it.

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Top ETL Tools: Unveiling the Best Solutions for Data Integration

Pickl AI

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. Scalability: It is suitable for enterprise-level data integration needs, offering scalability for handling large datasets efficiently. Read More: Advanced SQL Tips and Tricks for Data Analysts.

ETL 40
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Ask HN: Who wants to be hired? (July 2025)

Hacker News

Prior to that, I spent a couple years at First Orion - a smaller data company - helping found & build out a data engineering team as one of the first engineers. We were focused on building data pipelines and models to protect our users from malicious phonecalls. Background in backend software engineering.

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