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

Data Engineering Podcast

With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Just connect it to your database/data warehouse/data lakehouse/whatever you’re using and let them do the rest.

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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

In 2010, they introduced Apache Kafka , a pivotal Big Data ingestion backbone for LinkedIn’s real-time infrastructure. To transition from batch-oriented processing and respond to Kafka events within minutes or seconds, they built an in-house distributed event streaming framework, Apache Samza.

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StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar

Data Engineering Podcast

With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. How have projects such as Kafka and Pulsar impacted the broader software and data landscape? When is Pulsar the wrong choice?

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Unified Streaming And Batch Pipelines At LinkedIn: Reducing Processing time by 94% with Apache Beam

LinkedIn Engineering

In the past, we often used lambda architecture for processing jobs, meaning that our developers used two different systems for batch and stream processing. This pipeline reads ProfileData; joins the data with sideTable and then applies a user defined function called Standardizer(); finally, writes the standardized result to databases.

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Building A Data Lake For The Database Administrator At Upsolver

Data Engineering Podcast

What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert.

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What is Data Ingestion? Types, Frameworks, Tools, Use Cases

Knowledge Hut

Lambda architecture: A combination of both batch and real-time processing, the lambda architecture has three layers. The lambda architecture ensures completeness of data with minimal latency. Streaming data to Elasticsearch server from different databases. It is useful for Big Data ingestion.

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An Overview of Real Time Data Warehousing on Cloudera

Cloudera

So they needed a data warehouse that could keep up with the scale of modern big data systems , but provide the semantics and query performance of a traditional relational database. Data streamed in is queryable in conjunction with historical data, avoiding need for Lambda Architecture. They chose to build their RTDW on Cloudera.