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Big / Bug Data: Analyzing the Apache Flink Source Code

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Applications used in the field of Big Data process huge amounts of information, and this often happens in real time. Naturally, such applications must be highly reliable so that no error in the code can interfere with data processing. To achieve high reliability, one needs to keep a wary eye on the code quality of projects developed for this area. The PVS-Studio static analyzer is one of the solutions to this problem. Today, the Apache Flink project developed by the Apache Software Foundation, one of the leaders in the Big Data software market, was chosen as a test subject for the analyzer.

So, what is Apache Flink? It is an open-source framework for distributed processing of large amounts of data. It was developed as an alternative to Hadoop MapReduce in 2010 at the Technical University of Berlin. The framework is based on the distributed execution engine for batch and streaming data processing applications. This engine is written in Java and Scala. Today, Apache Flink can be used in projects written using Java, Scala, Python, and even SQL.

Source de l’article sur DZONE

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4 Ways Big Data Is Evolving Risk Management

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In the digital era, Big Data has drastically changed the landscape of business and risk management. With unlimited access to information about potential customers and user behavior, companies are using analytics to improve their risk management practices in more advanced ways than ever before.


Big Data Analytics

Techwave’s Big data analytics consulting services help you maximize revenue options and win loyal and happy customers.

Why Big Data Is Important

Big data has been around a long time, but it has taken a while for organizations to see the usefulness of big data. Big data doesn’t just track the consumer when they are online – it provides a history of behaviors that big data services can analyze and extrapolate from. If the consumer uses smart devices, makes a purchase with credit cards or checks, or visits establishments that use smart devices, they leave a data trail that can be analyzed by big data consulting to determine possible trends. These trends help businesses understand what drives their customers to make certain purchases over others.

Source de l’article sur DZONE

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6 Pillars of a Successful AI Strategy

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Machine learning and artificial intelligence, in general, have been on everyone’s lips for some time now. While the topic of AI is in the foreground in the media, most people (especially the management) still don’t know how machine learning is best applied.

Ultimately, machine learning can be described as a synergetic relationship between man and machine. Machine learning in practice requires the application of the scientific method and human communication skills. Successful companies have the analytical infrastructure, know-how, and close collaboration between analysts and business professionals to translate these synergies into ROI.

Source de l’article sur DZONE

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Streaming ETL With Apache Flink — Part 3

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Previous posts – Part 1  |  Part 2.

Introduction

In this post, let us start exploring Flink to solve a real-world problem. This post from zalando.com shows how they are using Flink to perform a complex event correlation. I will take a simplified and practical event correlation problem and try to solve using Flink.

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Talk to Your Database

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Talk to Your Database
DISCLAIMER: This post in based on personal experiences and the situations explained here may not apply in other context.

Figures displayed on the examples are just samples for demo purposes, not actual data.

In every company in the world, employees need access to information. Most companies purchase and install expensive software solutions or even spend years developing complex reporting systems on-site.

However, they all fall short satisfying user needs. They are either too complex, and non-technical people can’t understand how to use those tools, or they are too user-friendly and they lack the flexibility these users need.

Source de l’article sur DZONE

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The Rise of Machine Learning

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The recent surge of data has empowered a field of computer science that uses statistical techniques to give computer systems the ability to learn: Machine Learning. Modern Machine Learning Algorithms are able to overcome strictly static program instructions and make data-driven predictions that help companies make decisions with minimal human intervention.

IDC forecasts that spending on Machine Learning will grow from $12 billion in 2017 to $57.6 billion by 2021. What’s more, Machine Learning patents grew at a 34 percent CAGR between 2013 and 2017, making it the third-fastest growing category of all patents granted.


Source de l’article sur DZONE (AI)

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