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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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Building A Data Lake For The Database Administrator At Upsolver

Data Engineering Podcast

Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform.

Data Lake 100
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Maintaining Your Data Lake At Scale With Spark

Data Engineering Podcast

Summary Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.

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

Rockset

Traditional Data Processing: Batch and Streaming MapReduce, most commonly associated with Apache Hadoop, is a pure batch system that often introduces significant time lag in massaging new data into processed results. Most processing in the Lambda architecture happens in the pipeline and not at query time.

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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

The current architecture is called Lambda architecture, where you can handle both real-time streaming data and batch data. Log files are pushed to Kafka topic using NiFi, and this Data is Analyzed and stored in Cassandra DB for real-time analytics. Upload it to Azure Data lake storage manually.