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Tips to Build a Robust Data Lake Infrastructure

DareData

Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals. In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently.

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Consulting Case Study: Job Market Analysis

WeCloudData

By leveraging data engineering techniques combined with a cloud toolchain, WeCloudData helped a client achieve a continuous flow of current job market data with analytical capabilities and dashboards to drive the business forward and stay competitive.

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Consulting Case Study: Job Market Analysis

WeCloudData

By leveraging data engineering techniques combined with a cloud toolchain, WeCloudData helped a client achieve a continuous flow of current job market data with analytical capabilities and dashboards to drive the business forward and stay competitive.

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ELT Explained: What You Need to Know

Ascend.io

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

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Rollups on Streaming Data: Rockset vs Apache Druid

Rockset

But while it’s easier to stream the data, analyzing it in real time still involves too much cost and complexity. Creating and maintaining real-time data pipelines is too hard, and even the most advanced cloud warehouses are too slow and expensive for real-time analytics. Batch processes simply don’t cut it.

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How Rockset Enables SQL-Based Rollups for Streaming Data

Rockset

Apache Kafka has made acquiring real-time data more mainstream, but only a small sliver are turning batch analytics, run nightly, into real-time analytical dashboards with alerts and automatic anomaly detection. The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics.

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AWS QuickSight vs Power BI: Top Differences & Similarities

Knowledge Hut

Big Data support The SPICE engine in QuickSight was built to handle huge datasets, making it suited for big data scenarios. Its capacity to handle large amounts of data increases its flexibility in business settings. QuickSight's SPICE engine stores the aggregated data in memory, allowing very fast query response times.

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