Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage). Azure SQL Database, Azure Data Lake Storage).

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Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

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Databand.ai

AUGUST 30, 2023

Automation plays a critical role in the DataOps framework, as it enables organizations to streamline their data management and analytics processes and reduce the potential for human error. This can be achieved through the use of automated data ingestion, transformation, and analysis tools.

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Ascend.io

MAY 31, 2023

Table of Contents The Common Threads: Ingest, Transform, Share Before we explore the differences between the ETL process and a data pipeline , let’s acknowledge their shared DNA. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

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Snowflake

MARCH 27, 2023

Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL. Workflows automates not only geospatial processes, but other data workflows as well.

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Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

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Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Despite Hadoop’s parallel and distributed processing, compute was a limited resource as well.

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Knowledge Hut

SEPTEMBER 29, 2023

Microsoft Data Engineer Certification is one such certification which is most sought after by professionals. By combining data from various structured and unstructured data systems into structures, Microsoft Azure Data Engineers will be able to create analytics solutions.

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AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

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Knowledge Hut

MARCH 28, 2024

They handle large amounts of structured and unstructured data and use Azure services to develop data processing and analytics pipelines. Role Level: Intermediate Responsibilities Design and develop big data solutions using Azure services like Azure HDInsight, Azure Databricks, and Azure Data Lake Storage.

Data Engineering 52

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AltexSoft

SEPTEMBER 21, 2023

The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization. Fluentd is a data collector and a lighter-weight alternative to Logstash.

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Ascend.io

MARCH 17, 2023

Data quality rules, or assertions, should be configurable at every processing step in every pipeline and evaluate every data record. Value Catching data problems in real-time avoids costly reruns and delays. It reduces the amount of rework arising from after-the- fact quality reviews and simplifies data workflows.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

Data quality rules, or assertions, should be configurable at every processing step in every pipeline and evaluate every data record. Value Catching data problems in real-time avoids costly reruns and delays. It reduces the amount of rework arising from after-the- fact quality reviews and simplifies data workflows.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

phData: Data Engineering

JANUARY 3, 2022

We recommend identifying sync points that align with your information architecture so that data currency expectations are known at a governance level. This helps drive requirements and determines the right validation at the right time for the data. Too slow and your system becomes backed up and consumers aren’t seeing their data.

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IT AWS Software Engineer Software Engineering 52

Data Engineering Weekly

DECEMBER 18, 2024

These New Age Data IDEs will be characterized by: Seamless Integration: They will seamlessly integrate the entire data lifecycle, from data ingestion and transformation to analysis, visualization, and deployment, all within a unified environment. Tools like lakebyte.ai are the beginning of such a revolution.

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