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Data Science Blogathon 30th Edition- Women in Data Science

Analytics Vidhya

The Biggest Data Science Blogathon is now live! Knowledge is power. Sharing knowledge is the key to unlocking that power.”― Martin Uzochukwu Ugwu Analytics Vidhya is back with the largest data-sharing knowledge competition- The Data Science Blogathon.

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Business Analytics vs Data Science: Which One Is Right for You?

Pickl AI

Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. They must also stay updated on tools such as TensorFlow, Hadoop, and cloud-based platforms like AWS or Azure.

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Data Science Career FAQs Answered: Educational Background

Mlearning.ai

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

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Data Warehouse vs. Data Lake

Precisely

Apache Hadoop, for example, was initially created as a mechanism for distributed storage of large amounts of information. Hadoop and Snowflake represent tremendous advances in analytics capabilities. Other platforms defy simple categorization, however. It is often used as a foundation for enterprise data lakes.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

This is an architecture that’s well suited for the cloud since AWS S3 or Azure DLS2 can provide the requisite storage. It can include technologies that range from Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data center, to name just a few.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

ETL Tools: Apache NiFi, Talend, etc. Big Data Processing: Apache Hadoop, Apache Spark, etc. Cloud Platforms: AWS, Azure, Google Cloud, etc. Data Warehousing: Amazon Redshift, Google BigQuery, etc. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.