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Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

Data Engineering Podcast

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Data Lake 100
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Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics

Rockset

Aggregator Leaf Tailer (ALT) is the data architecture favored by web-scale companies, like Facebook, LinkedIn, and Google, for its efficiency and scalability. In this blog post, I will describe the Aggregator Leaf Tailer architecture and its advantages for low-latency data processing and analytics.

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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

In this guide, we’ll explore the patterns that can help you design data pipelines that actually work. Table of Contents Common Data Pipeline Design Patterns Explained 1. Batch Processing Pattern 2. Stream Processing Pattern 3. Lambda Architecture Pattern 4. Kappa Architecture Pattern 5.

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Data Ingestion: 7 Challenges and 4 Best Practices

Monte Carlo

Data from these sources are often ingested into a cloud-based data warehouse or data lake , where they can then be mined for information and insights. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley. Some data teams will leverage micro-batch strategies for time sensitive use cases.

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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

to accumulate data over a given period for better analysis. There are many more aspects to it and one can learn them better if they work on a sample data aggregation project. Project Idea: Explore what is real-time data processing, the architecture of a big data project, and data flow by working on a sample of big data.