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Upgrade your Modern Data Stack

Christophe Blefari

The era of Big Data was characterised by Hadoop, HDFS, distributed computing (Spark), above the JVM. That's why big data technologies got swooshed by the modern data stack when it arrived on the market—excepting Spark. We need to store, process and visualise data, everything else is just marketing.

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

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15+ Best Data Engineering Tools to Explore in 2023

Knowledge Hut

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools. What are Data Engineering Tools?

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How ATB Financial is Utilizing Hybrid Cloud to Reduce the Time to Value for Big Data Analytics by 90 Percent

Cloudera

The company sought a data management platform that would allow its enterprise to handle greater data variety, velocity and volume in a cost-effective manner. Enabling this transformation is the HDP platform, along with SAS Viya on Google Cloud , which has delivered machine learning models and personalization at scale.

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SQL for Data Engineering: Success Blueprint for Data Engineers

ProjectPro

According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL , Hadoop , and other big data technologies, SQL remains the most dominant language for data operations among all tech companies.

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Creating a Data Pipeline with Spark, Google Cloud Storage and Big Query

Towards Data Science

On-premise and cloud working together to deliver a data product Photo by Toro Tseleng on Unsplash Developing a data pipeline is somewhat similar to playing with lego, you mentalize what needs to be achieved (the data requirements), choose the pieces (software, tools, platforms), and fit them together.

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Business Intelligence vs Business Analytics: Difference Stated

Knowledge Hut

Because it involves analyzing large amounts of data from multiple sources, business analytics can be a time- and resource-intensive process. Tools Business intelligence uses various tools to collect, analyze, and report data. Business analytics uses predictive models to forecast future trends.