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

Data Engineering Podcast

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. 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.

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

LinkedIn Engineering

To enable the ingestion and real-time processing of enormous volumes of data, LinkedIn built a custom stream processing ecosystem largely with tools developed in-house (and subsequently open-sourced). In 2010, they introduced Apache Kafka , a pivotal Big Data ingestion backbone for LinkedIn’s real-time infrastructure.

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Handling Bursty Traffic in Real-Time Analytics Applications

Rockset

Lambda Architecture: Too Many Compromises A decade ago, a multitiered database architecture called Lambda began to emerge. Lambda systems try to accommodate the needs of both big data-focused data scientists as well as streaming-focused developers by separating data ingestion into two layers.

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

Knowledge Hut

An end-to-end Data Science pipeline starts from business discussion to delivering the product to the customers. One of the key components of this pipeline is Data ingestion. It helps in integrating data from multiple sources such as IoT, SaaS, on-premises, etc., What is Data Ingestion?

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Data Ingestion: 7 Challenges and 4 Best Practices

Monte Carlo

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion? Decision making would be slower and less accurate.

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Apache Spark Use Cases & Applications

Knowledge Hut

Apache Spark and MLlib is being used by a lot of these companies to capture real-time sales and invoice data, ingest it and then figure out the inventory. Conclusion Apache Spark has capabilities to process huge amount of data in a very efficient manner with high throughput. All these pose huge technical challenges.

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Data Pipeline Architecture: Understanding What Works Best for You

Ascend.io

As companies become more data-driven, the scope and complexity of data pipelines inevitably expand. Without a well-planned architecture, these pipelines can quickly become unmanageable, often reaching a point where efficiency and transparency take a backseat, leading to operational chaos. What Is Data Pipeline Architecture?