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Apache Spark and Hadoop MapReduce are two popular big data processing frameworks used in the industry. Both of these frameworks have the capability to handle large-scale data processing, but they differ in terms of their architecture and design.

The data we generate, store, and share is growing exponentially as the world inexorably digitizes. With the global data sphere expected to double in size by 2026 as organizations and consumers increasingly go online, automate, and digitize processes, the right tools are required to mine this massive trove of valuable data coming from a widening and diverse pool of sources globally. The competitive edge gained by rapidly converting complex data into business insights is a crucial growth driver.

Running a Shopify ecommerce store can be challenging - you have to take care of marketing, supplies, deliveries, returns, complaints, and every little thing in between, so you easily lose focus on what matters and brings value. Luckily, we have an easy solution! Keboola and Snowflake have partnered to bring you a no-code solution that turns your Shopify data into an insights machine in just 45 minutes. Let's take a look and try it hands-on! You might say.

Over the past few years, there has been growing concern about the lack of innovation among corporations. This lack of innovation is a major problem for businesses, as it can lead to stagnation, decreased competitiveness, and even obsolescence. As the pace of technological change continues to accelerate, it's becoming increasingly important for companies to find ways to foster innovation and stay ahead of the curve.

Every so often, different advocates across organizations ignore the Voice of the Customer. This may be due to changes in business priorities, redistribution of resources, focus on new trends, or that they clear a profit regardless. This brings the value of the customer's voice into question: should we still allocate time and effort towards listening to customers when following new trends is the norm? ‍ The short answer is a resounding yes.

When I was working at Google back in the mid 2000’s, we dealt with tens of billions of ad impressions a day, trained several machine learning models on years worth of historic data, and used frequently-updated models in ranking ads. The whole system was an amazing feat of engineering and there was no system out there that was even close to handling this much data. It took us years and hundreds of engineers to make this happen, today, the same scale can be achieved in any enterprise.