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Hybrid Vs. Multi-Cloud: 5 Key Comparisons in Kafka Architectures

Smart Data Collective

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. 5 Key Comparisons in Different Apache Kafka Architectures. 5 Key Comparisons in Different Apache Kafka Architectures.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Python, SQL, and Apache Spark are essential for data engineering workflows. Real-time data processing with Apache Kafka enables faster decision-making. Without data engineering , companies would struggle to analyse information and make informed decisions. What Does a Data Engineer Do?

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Navigating the Big Data Frontier: A Guide to Efficient Handling

Women in Big Data

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes.

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How to Unlock Real-Time Analytics with Snowflake?

phData

Leveraging real-time analytics to make informed decisions is the golden standard for virtually every business that collects data. What is Apache Kafka, and How is it Used in Building Real-time Data Pipelines? Apache Kafka is an open-source event distribution platform. Example: openssl rsa -in C:tmpnew_rsa_key_v1.p8

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What is Data Ingestion? Understanding the Basics

Pickl AI

This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. From extracting information from databases and spreadsheets to ingesting streaming data from IoT devices and social media platforms, It’s the foundation upon which data-driven initiatives are built.

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How Thomson Reuters delivers personalized content subscription plans at scale using Amazon Personalize

AWS Machine Learning Blog

Thomson Reuters (TR) is one of the world’s most trusted information organizations for businesses and professionals. TR used AWS Glue DataBrew and AWS Batch jobs to perform the extract, transform, and load (ETL) jobs in the ML pipelines, and SageMaker along with Amazon Personalize to tailor the recommendations.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.