Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.
At GrowingData, we have been working with “big data” for the past 10 years, from financial market data, through to hundreds of millions of transactions and terabytes of text data. We understand and have used platforms such a Red Shift, Hadoop all the way through to super computers to deliver systems that separate the signal from the noise.
The cloud refers to scalable computing services managed and hosted in multiple redundant offsite locations.
At Growing Data, we have been using Amazon Web Services since 2008, and Microsoft’s Azure platform since 2012. We see fantastic benefits to moving data infrastructure into the cloud, from cost savings, improved reliability, superior scalability and improved performance.
Machine learning is a collection of statistical methods used to build data models that provide deep insight and predictive capabilities.
We have successfully used machine learning in cancer research, detecting trends in financial markets, matching people to job the jobs of their dreams and predicting when customers will cancel memberships with our clients.
Analytics relates to the discovery and communication of meaningful patterns in data.
At Growing Data, we have built analytics platforms for characterizing mutations in cancer, reporting on customer loyalty and financial market information. From data collection, processing, dashboards and notification systems, we have tackled every aspect of building analytics systems from the ground up.
Data is inherently difficult to understand and communicate without tools to visualize it.
From high level dashboard through to graph and 3d visualizations, Growing Data has the know how to communicate whatever story your data is trying to tell. We specialize in using technologies such as D3.js to bring visualizations to life within your web browser.
Data Warehouse is less fashionable than it once was, but the value proposition of a single version of the truth across organizational areas remains strong. Even as “big data” and schemaless data stores promise to make the data warehouse a thing of the past, this promise is far from being realized. Doing the work upfront to design a proper schema, cleansing and loading processes delivers enormous value to downstream consumers of data, by making their data more accurate and easier to understand. The data warehouse is far from dead!