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3 Key Infrastructure Considerations for Your Big Data Operation

In this special guest feature, AJ Byers, President and CEO, ROOT Data Center, suggests that if your organization is launching or expanding a Big Data initiative, it would be wise to keep the needs of real estate, power and up-time top-of-mind. Whether your Big Data operations ultimately reside on-premises, at a colocation data center, or in the cloud, infrastructure that is flexible, scalable, sustainable and reliable is ground zero for ensuring its success.

“Above the Trend Line” – Your Industry Rumor Central for 4/23/2018

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Data Capture Challenges No One Wants to Talk About

In this contributed article, technology writer and blogger Kayla Matthews discusses 3 data capture challenges familiar to IT professionals and data scientists. There are numerous challenges IT professionals and data scientists encounter when striving for accurate and efficient data capture. But unfortunately, many of these challenges don’t always get the level of attention and discussion they deserve, despite having a significant impact on data capture.

3 Reasons Why Machine Learning Should Matter to B2C Businesses Too

In this contributing article, Pratik Dholakiya, Co-Founder, CMO of E2M, provides 3 reasons why machine learning should matter to business-to-consumer (B2C) businesses too. There is a lot of talk about how machine learning, AI, and big data can be used to help B2B companies improve their efforts. While there’s no disputing this, there is a large misconception that B2C businesses cannot be helped in the same way. This is, of course, quite false.

Big Data Analytics Receive a “Spark” In the Arm

In this special guest feature, Anand Venugopal, head of StreamAnalytix at Impetus Technologies, discusses real-time streaming analytics applications and how companies can use Apache Spark for data processing and analytics functionality. Real-time data and analytics processes are the central nervous system of today’s enterprise, which makes it no surprise that the global revenue in the business intelligence (BI) and analytics software market is forecast to reach $22.8 billion by the end of 2020.

Developing a Deeper Understanding of Apache Kafka Architecture Part 2: Write and Read Scalability

In this contributed article, Paul Brebner, Tech Evangelist at Instaclustr provides Part 2 of his two-part series on Apache Kafka. In Par 1 we gained an understanding of the main Kafka components and how Kafka consumers work. Now, we’ll see how these contribute to the ability of Kafka to provide extreme scalability for streaming write and read workloads.

New Tech Trends Survey: AI/ML Top List of Priorities for IT Pros

SADA Systems, a leader in providing business and technology consulting services, announces the results of a survey of IT professionals and their opinions on some of the most talked-about technologies so far in 2018. The survey, which was completed by 500 IT professionals including nearly 100 top IT executives, focused on six technology categories – artificial intelligence/machine learning, augmented reality, blockchain, edge computing, the internet of things (IoT), and virtual reality.

How We Implemented (and Secured) a Big Data Microservices Infrastructure

In this contributed article, Tobias Gurtzick, Security Architect, and Dr. Sayf Al-Sefou, CTO at Arvato Infoscore, discuss the pretty lofty big data challenge stemming from their company’s monolothic architecture, and how they got through it (with particularly data sensitivity being in the financial services space and thus subject to tighter regulations).

SWIM.AI Introduces SWIM EDX, Software Delivering Real-Time Intelligence and Digital Twins at the Edge

SWIM.AI, the edge intelligence software company, exited stealth and announced the general availability of its edge software solution, SWIM EDX™, delivering edge intelligence and real-time business and operational insights. Current business intelligence solutions require large volumes of time-series data to be reduced, streamed to the cloud, stored, cleaned, analyzed and then modelled – at great expense and often with significant delays. By applying analytics, self-training digital twins and edge computing, SWIM EDX allows manufacturers, infrastructure providers, enterprises, cities and IoT vendors to analyze and take immediate action on fast edge data.

Developing a Deeper Understanding of Apache Kafka Architecture

In this contributed article, Paul Brebner, Tech Evangelist at Instaclustr provides an understanding of the main Kafka components and how Kafka consumers work. The Apache Kafka distributed streaming platform features an architecture that – ironically, given the name – provides application messaging that is markedly clearer and less Kafkaesque when compared with alternatives.