As many are pointing out, the pandemic is causing major global distribution shifts. For those working with advance analytics, machine learning and artificial intelligence programs, this pandemic is messing up any confidence of historical data used with predictive analytics models and intelligent automation. Corporate KPIs aren’t the same. Pretty much anything that attempts to model human behaviour in the slightest, directly or indirectly, is changing. Going forward, your analytics and models will need to be far more nimble and robust than in the past.
So where should CIOs and businesses focus their time & effort now? Maybe, like the airline industry is taking this time to spruce up planes (thank you, much needed), departments focused on supporting data science programs need to take this time to shore up their data infrastructure.
Welcome to the world of Apache Spark open source data science. If you didn’t already know, some of our world’s problems like fraud detection, precision level personalization of many online services (Netflix, Amazon, Google etc) and scientific research are being addressed with Apache Spark. In addition to it’s real-time processing power and high-speeds, it’s supported by a community that continues to innovate and evolve its many applications and APIs – making this platform the most futuristic, “next gen” & forward leaning in data science.
Why move to Apache Spark now? It’s all about the money. Once this pandemic is behind us, your going to need funding to advance new ideas & opportunities that Covid-19 reveals. Where is this investment going to come from? From the substantial cost savings of moving to Apache Spark.
Many large organizations are spending tens of millions on legacy data science platforms. Apache Spark, for all intents and purposes is free software. Moving to Spark fast is the key to getting access to these savings. And think about putting the data processing power of Spark into your data engineers and data scientists hands now – at a time where coming up with your next big idea is most critical.
How can you accelerate your adoption of Apache Spark? Automate your SAS to PySpark code conversion.
Introducing SPROCKET – the world’s only SAS to PySpark automated migration solution. It converts your legacy SAS code to PySpark – allowing you to fully adopt Apache Spark with production ready PySpark code. It’s fast, simple and accurate.
When a brute force approach will take you months, even years to convert your code, SPROCKET will deliver you production ready, consistent code in a fraction of the time.
Reap the rewards Apache Spark and prepare yourself for the power of open source data science.
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