The future of data science can be confusing – there’s so many options when it comes to advanced analytics. Big data initiatives have been my focus for decades now and I think I have an idea of where things are headed. Of course, this is just my opinion but here’s a few thoughts on the future of data science.
We live in a world of big data and analytics is all about the complex process of examining large and varied data sets to inform optimal decisioning, predictive modelling, NLP, adhoc analysis and dashboarding. Key challenges Enterprises face in doing so include:
1. Disparate technologies with dozens of software frameworks create an “alphabet soup” infrastructure, and make managing SLAs a nightmare
2. Multiple copies of inconsistent data with unreliable pipelines create data quality & reliability constraints
3. End to end security continues as a paramount concern in managing access to both structured and unstructured data, challenging governance from the lack of standards across environments
Many make the list of “top 20” or “top 50” data science platforms but few combine the power of being a unified analytics engine for large scale data processing AND are supported by the most actively developed open source community that continue to innovate and solve complex intelligent automation tasks fueling ML and AI.
This is why big data processing is seeing Apache Spark as the market leader. It supports machine learning, streaming data, SQL and graph processing. Spark is solving some of our world’s big problems like fraud detection, precision level personalization of many online services and scientific research. Today, most sectors like banking, telecommunications, healthcare, government and insurance are using Spark for real-time analysis. Tech giants like Amazon, Microsoft, IBM, Apply and Facebook leverage Spark.
Despite this, Spark isn’t widely acknowledged as the future of data science. Why should Enterprises look to Apache Spark for their big data strategy? Here’s a few reasons:
# 1 – it’s free
# 2 – it delivers high quality data, processing faster than anything else on the market (100X faster than Hadoop)
# 3 – it supports a wide range of advanced analytics & intelligent automation tasks via APIs, standing out as a computing engine that can be used with a wide range of storage systems (AWS, Azure etc), and processing both structured and unstructured data (Spark doesn’t care where the data is)
If your convinced about moving to Spark, your challenge will be getting your legacy data off of wherever it is – fast – to avoid paying for renewals or ongoing proprietary licensing. How can you fast track your adoption of Apache Spark? Automate your SAS to PySpark code conversion. Wise With Data’s SPROCKET is 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 weeks/months.
Reap the rewards Apache Spark and prepare yourself for the power of open source data science.
Want to know more? Contact me at firstname.lastname@example.org