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Data analytics: your complete guide to big data consulting

Agile Engine

From emerging trends to hiring a data consultancy, this article has everything you need to navigate the data analytics landscape in 2024. What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7. Table of contents 1.

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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Big Data Analytics: How It Works, Tools, and Real-Life Applications

Altexsoft

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

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The Good and the Bad of Apache Spark Big Data Processing

Altexsoft

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics.

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Analytics Maturity Model: Levels, Technologies, and Applications

Altexsoft

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Introducing data engineering and data science expertise.

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The Good and the Bad of Apache Kafka Streaming Platform

Altexsoft

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. process data in real time and run streaming analytics. High availability and fault tolerance.

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Optimizing Connected Logistics Operations with Data Analytics

Trigent

Thankfully, it’s now a game for new technologies and leveraging structured and unstructured data like no other. The future of the global supply chain market lies in IoT, integrated solutions, data, and mobility. Connected logistics devices generate a massive amount of data. Enhance transparency.