Data integration is one of the most crucial resources within every business. As a matter of fact, data is one of the most expansive and complex resources that's difficult to process and manage. As a business expands, the amount of exact data it needs to integrate, analyze, and load will also grow. Organizations continue to add new, disparate systems as well as applications at a fast pace. Which ultimately increases data volume.

The Data Integration Platform team at Wayfair is responsible for enabling teams by solving for their data needs to drive Wayfair's next $50B growth. Our team is looking for a smart, passionate, and curious Engineering Manager to help us scale our platform that provides data movement and processing capabilities to teams and individuals. With the broad set of technologies we are using, the scale at which our systems operate, and a diverse set of stakeholders we serve, you will have the opportunity to grow your skills and be exposed to new people and ideas, while playing a major role in Wayfair's growth. If you are the type of person who is fascinated by engineering extremely large deployments of high-volume systems, skilled in designing scalable automation, and are passionate about project delivery and stakeholder management, we should talk. Wayfair is one of the world's largest online destinations for the home.

Manufacturing and processing plants might not be at the front of anyone's minds when it comes to tech adoption, but as illustrated by a recent IDC report, The Worldwide Internet of Things Spending Guide, the manufacturing industry is transforming into industry 4.0 and spearheading the adoption of IoT. Industry 4.0 is the newest industrial revolution, bringing automation, big data and AI into plants and factories around the world. One of the building blocks of industry 4.0 is the internet of things, or IoT. A recent report forecast that spending on IoT platforms would see a 40% CAGR between 2019 and 2024, resulting in spending that exceeds $12.4 billion. In 2019, leading industry corporations were expected to invest almost $200 billion in IoT solutions.

I'm sure you will be shocked (shocked!) to learn that this integration was not as simple as the Sales person thought. In fact, this work internally cost us over $40K to develop. And no, the sales person did not help with the coding. This story came to mind as I've gotten to know Attunity, Qlik's most recent acquisition. It's enabled Qlik to introduce our Data Integration Platform, a combination of Attunity products and Qlik Data Catalyst.

Click to learn more about author Daniel Jebaraj. Data Science is now being used as a competitive weapon. As with other technologies and processes that can transform the way companies operate, there's a lot of contradictory information about it that's causing considerable confusion. Most of today's business leaders have heard that Data Science can improve operational efficiency and customer relationships, but it isn't always clear how Data Science should be implemented or what the specific business benefits might be. This blog post addresses some of the misunderstandings individuals and organizations have about Data Science. It also includes tips developers can use to enable Data Science capabilities in their organizations.

Over a year ago, my fellow data infrastructure engineers and I broke ground on a total rewrite of our event delivery infrastructure. Our mission was to build a robust, centralized data integration platform tailored to the needs of our Data Scientists. The platform would be fully self-service, so as to maximize the Data Scientists' autonomy and give them complete control over their event data. Ultimately, we delivered a platform that is revolutionizing the way Data Scientists interact with Stitch Fix's data. In two parts, this post peeks into Stitch Fix's Data Science culture and delves into how it drove the fundamental decisions we made in our lowest levels of data infrastructure. Part 1 discusses our design process, explains our guiding philosophy around self-service tooling and explores our data integration platform concept. Part 2 is a technical dive into the decisions we made and a walk-through of the whole architecture.

In this special guest feature, Vince Padua, VP of Platform Innovation, Technology and Design at Axway, discusses the emergence of "digital twin" technology which will revolutionize how industrial enterprises approach manufacturing operations. Digital twins unite physical entities with virtually-modeled "twins" based on technologies like AI and Big Data derived from IoT sensors, ultimately improving the design and execution of manufacturing and maintenance life cycles as well as creating new revenue streams and services. Vince is a platform and product executive spanning cloud, mobile, big data, analytics, and artificial intelligence offerings. He is a leader of global product management, design, and GTM teams that consistently delivered outstanding business results. The myriad uses of big data continue to unfold as new methods of generating, parsing and combining it evolve.

The growing importance of business intelligence and data analytics applications in driving business decision making has made data integration's vital role in the enterprise crystal clear. From gathering data, transforming it into useful information and delivering it to the business users or processes that need it, data integration routines provide the crucial link between a variety of source and target systems. As the first article in this series examined, several types of packaged software have emerged to meet the challenges of data integration. The current generation of data integration tools consists of full-fledged suites that support extract, transform and load (ETL) processes, application integration, cloud-based and real-time integration, data virtualization, data cleansing and data profiling. How can you determine if your organization should invest in a data integration tool?