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An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP.

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Data Ingestion: 7 Challenges and 4 Best Practices

Monte Carlo

Also worth noting is lambda architecture-based data ingestion which is a hybrid model that combines features of both streaming and batch data ingestion. For example, financial services companies analyzing constantly-changing market information, or power grid companies that need to monitor and react to outages in real-time.

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Apache Spark Use Cases & Applications

Knowledge Hut

It does work with a variety of other Data sources like Cassandra, MySQL, AWS S3 etc. It can solve problems related to batch processing, near real-time processing, can be used to apply lambda architecture, can be used for Structured streaming.

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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

The primary step in this data project is to gather streaming data from Airline API using NiFi and batch data using AWS redshift using Sqoop. You will then compare the performances to discuss hive optimization techniques and visualize the data using AWS Quicksight. You will use AWS EC2 instance and docker-composer for this project.