SERVICES
Data Mining
Streaming Analytics
Data mining is, in our view, the most important stage in the world of Big Data analytics. Typically, corporations have huge amounts of data and information in databases, emails, server logs, etc., that accumulate daily and do not know how to extract and structure information so it can be useful to the business.
Our data mining service seeks to solve this problem by the extraction, processing and visualization of huge amounts of data on a quick and economically efficient manner.
Tools: Hadoop, Spark, Hbase, R, Python, AWS S3, Azure Data Lake, HDInsight, SQL.
Big Data analysis is divided in two types: Batch and Streaming (real time). Each of these require different paradigms and different tools.
The goal of analyzing data streams in real time, it is to make decisions and respond to events immediately, for example: approve or deny online credit applications based on user backgroung and historic information, recommend items to users based on search activity and past views in an online store, buy or sell shares in the sotck market ("algorithm trading"), etc.
Our Streaming Analytics service seeks to provide this capability to corporations, using the cloud as the scalable infrastructure.
Tools: Apache Storm, Kafka, Azure Stream Analytics, Spark Streaming, AWS Kinesis, Google Dataflow.
Business Intelligence & Data Warehousing
Business intelligence or BI, is a general term that refers to a variety of software applications used to analyze the data of an organization. BI as a discipline that consists of several activities, including data mining, online analytical processing, querying and reporting.
Our Business Intelligence Service aims to effectively communicate the information insights contained in the BIG DATA to the decision makers of the company. It Usually consists of a mixture of Data Warehousing and online reporting software.
Tools: Microsoft PowerBI, Azure SQL DW, AWS Redshift, Tableau, Periscope, Jupyter Notebook Dashboards.
Cloud Architecture for scale
To process large amounts of data or "Big Data" it is necessary to use parallel processing, ie using multiple servers and parallelizing the data. The advent of cloud computing has enabled the cost of using hundreds or thousands of servers in parallel only be a fraction of what it used to be in the past.
Our design and implementation of the "cloud" architecture consists in defining the elements in Azure, AWS and Google cloud, so that the application is (1) Scalable, (2) Low Cost and (3) Fast.
Tools: Azure Data Lake, Event Hubs, Azure SQL DW y HDInsight, AWS Kinesis, S3, Spot Instances, EC2, Redshift, EMR, Google Pub/Sub, BigQuery, Dataflow, DataProc, DataLab.
Machine Learning
After extracting, transforming and analyzing the BIG DATA, usually what companies need is: automated decisions. This is achieved through Machine Learning.
Machine learning is the science that gives computers the ability to learn without being explicitly programmed.
Our Machine Learning service provides our customers the ability to implement algorithms for classification, regression and recommendation and come up with a smart app design.
Tools: H2O.ai, Tensorflow, Theano, Keras, Lasagne, Spark MLLib, Azure ML, XGBoost.
Web and Mobile App development
Our Application Development service is in summary: taking to production , in the form of a web or mobile app, the automated process of big data extraction, analysis and prediction.
The process of creating intelligent cloud applications that are scalable, fault-tolerant and cross-platform requires much more than programming knowledge. Today, it requires a multi-disciplinary team including: DevOps, Data Scientists, Programmers, QA , and UI programmers.
See our demos for examples of our smart app service..
Tools: MS Visual Studio, Azure App Service, Xamarin, .NET, Node.JS, Python, Scala, Maven, AWS CodeDeploy and Elastic BeansTalk, Git, Github, Angular.JS, Backbone.JS, Flask.