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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

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Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts.

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Data Scientist vs Data Engineer: Differences and Why You Need Both

AltexSoft

Data engineers are programmers first and data specialists next, so they use their coding skills to develop, integrate, and manage tools supporting the data infrastructure: data warehouse, databases, ETL tools, and analytical systems. Providing data access tools. ETL and BI skills.

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5 Key Takeaways from Flink Forward 2023

Cloudera

2: The majority of Flink shops are in earlier phases of maturity We talked to numerous developer teams who had migrated workloads from legacy ETL tools, Kafka streams, Spark streaming, or other tools for the efficiency and speed of Flink. For now, Flink plus Iceberg is the compute plus storage solution for streaming data.

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Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]

Knowledge Hut

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB.

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10 Essential Azure Data Engineer Skills to Improve in 2023

Knowledge Hut

They enhance data pipelines, transform data, and guarantee the accuracy, integrity, and compliance of the data. Their job entails Azure data engineer skills like using big data, databases, data lakes, and analytics to help firms make efficient data-driven decisions.

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Why the Data Journey Manifesto?

DataKitchen

We had been talking about “Agile Analytic Operations,” “DevOps for Data Teams,” and “Lean Manufacturing For Data,” but the concept was hard to get across and communicate. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science.