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Complex Event Processing (CEP)

Dataconomy

Complex Event Processing (CEP) is at the forefront of modern analytics, enabling organizations to extract valuable insights from vast streams of real-time data. As industries evolve, the ability to process and respond to events in the moment becomes mission-critical. What is Complex Event Processing (CEP)?

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Streaming Langchain: Real-time Data Processing with AI

Data Science Dojo

Learn to build a recommendation system using Python Real-Time Interaction Whether it’s engaging with customers, analyzing live events, or responding to user queries, streaming enables more natural, responsive interactions. or later Install Langchain: Ensure that Langchain is installed in your Python environment.

AI 366
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Learn How to Build Airtight Data Pipelines for your AI Initiatives

databricks

"I can't think of anything that's been more powerful since the desktop computer." — Michael Carbin, Associate Professor, MIT, and Founding Advisor, MosaicML A.

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Streaming Data Pipelines: What Are They and How to Build One

Precisely

Business success is based on how we use continuously changing data. That’s where streaming data pipelines come into play. This article explores what streaming data pipelines are, how they work, and how to build this data pipeline architecture. What is a streaming data pipeline?

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Airbyte: The ultimate workhorse for all your ELT pipelines

Data Science Dojo

Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment. Free to use. Conclusion  There are a ton of small services that aren’t supported on traditional data pipeline platforms.

Azure 370
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Exploring the Power of Microsoft Fabric: A Hands-On Guide with a Sales Use Case

Data Science Dojo

Let’s explore each of these components and its application in the sales domain: Synapse Data Engineering: Synapse Data Engineering provides a powerful Spark platform designed for large-scale data transformations through Lakehouse. Here, we changed the data types of columns and dealt with missing values.

Power BI 284
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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

Flipboard

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.

ETL 139