Demos de Big Data y Machine Learning de Stratebi

Real Time

Real Time

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Redes Sociales

Social Network

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Redes Sociales

Big Data Analytics

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Internet of Things

Internet of Things

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Graph Db

Graph Analysis

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Machine Learning

Machine Learning

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Web Semántica

Web Semantic

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spark_icono Spark Streaming
kafka_icono Kafka
olap_icono OLAP Analytics
with Kylin and STpivot
tableau_icono Cuadro de Mando
with Kylin & Tableau
power-bi_icono Cuadro de Mando
with Kylin & Power BI
superset_icono Cuadro de Mando
with Kylin & Superset
twitter_icono Twitter
twitter_h_icono Twitter Hashtag
websearch_icono Web Search
neo4j_icono Panama Papers - Neo4j
neo4j_icono Route Optimization - Neo4j
ml_icono Films Recommendation
ml_icono Sales prediction
ml_icono Sales prediction (Notebook)
ml_icono Route Tracker
power-bi_icono Power BI Dashboard
with Talend & Vertica

I+D+i BigData

In StrateBI we believe in the value of Big Data technologies for data processing and the possibility of obtain knowledge using it, with the goal of making easier the process of decisions in any industry. Our team makes a great job on I+D+i in Big Data


We keep updated about news and scientific articles published about Big Data technologies.

Its made with emerging ones that we think have a great potential, as well as the consolidated ones.

With this, we detect new features that can improve the behavior or performance of our solutions.


We put in practice the results of the research phase.

We deploy the improvements and validate its application in real use cases, similar to the ones we show in this demo.


Once we test the usefulness and robustness of improvements or new features added we introduce in our solutions in different projects.

In this way StrateBI guarantees the use of cutting edge Big Data technologies, previous tests and improvements by out I+D+i in Big Data

Used Technologies


Apache Hadoop is the most popular Big Data environment, it allows the distributed computing on clusters with commodity hardware and low cost.

The basic and default configuration for a Hadoop cluster includes distributed storage of data using (HDFS), a resource manager (YARN) Yet Another Resource Negotiator, and running on top of this one, is the (Map Reduce) framework, that perform the distributed processing of data.

Besides these components, there are another set of higher level tools, for storing and processing data, like Hive or Spark, as an example. They offer the abstraction that simplifies the development for that environment.

As mentioned before, Hadoop is the most popular Big Data environment, the reason is because it offer a wide range of technologies and a very high robustness level. It is ideal for the new concept of Data Lake for the later analytics using powerful BI tools.


Flume is a distributed and trustworthy system for the efficient collection, aggregation and processing of Streaming Data.


Kafka is a distributed message system that use the pattern publish-subscribe, is fault tolerant, horizontal scalable and is ideal for Stream Data Processing

hortonworks cloudera

To make easier the management, installation and maintenance of hadoop cluster we work with two main Hadoop Distributions.

A hadoop distribution is a software package, that include the basic components of Hadoop, with a plus of other technologies, frameworks and tools and the possibility of installing using a web application.

About this, in Stratebi we recommend the use of a hadoop distribution. Being Hortonworks and Cloudera the leader distributions currently in the market. For this reason our demo is running over a Cloudera distribution and a Hortonworks distribution.

spark spark streaming

Spark implements the Map Reduce programming paradigm making intensive usage of RAM memory instead of disk.

Using Spark, we can improve the performance of Map Reduce applications by implementing iterative algorithms, machine learning (MLib), statistics analysis R module, or real time analytics Spark Streaming, all this is icluded in our demo.