Big Data, BI & ML

We can improve your business insights with interactive dashboards, built on your current systems or on Big Data platforms.

Data engineering

The planet is generating information at increasing rates, and consequently, setting out huge challenges in its storage, treatment and confidentiality.

However, the biggest challenge for companies is giving value to their own data. For that reason, it is necessary to avoid its dispersion in data silos or heterogeneous sources of information and store them in an integrated Big Data platform, where information could be consulted and easily analised.

We can group this platforms in two levels, depending on the data volume that has to be stored and on the required transformations: Data lakes and Datawarehouses.

  • A Data Lake is data repository which contains mainly raw data or data which has not been treated deeply. The storage in these systems is usually based in distributed file systems with high capacity such as HDFS, AWS S3, Google Cloud Storage or Azure Data Lake. All of them are able to storage structured information (CSV, JSON) and not structured data (raw text or binaries). While some of these systems guarantee the scalability in storage terms, Hadoop and Spark guarantee the scalability in processing terms using computing paradigms like MapReduce. These paradigms are virtually independent of the volume of data to be processed. On top of these, Hive and Spark SQL allow the access to the data presented in tables and offers views through SQL sentences, being also possible to use HBase as a low-latency columnar database. Usually, these kind of components can be deployed and managed in an integrated way through commercial distributions, such as Cloudera, AWS EMR, Azure Data Lake Storage + Google Cloud SQL, being these some of the most popular ones. These systems also operate in Petabytes and even further.

  • A Datawarehouse manages structured information obtained through transformations and aggregations operated on the raw data, usually in order to adapt them to a data model,stablished according the company guidelines of Data Governance. These platforms do not manage as massive amounts of data as Data Lakes and its range of operation is closer to Terabytes. Some examples of products that are able to implement a datawarehouse from raw data are Apache Druid, AWS Redshift and Azure Synapse Analytics.

The treatment of data in real time deserves an specific section. Apache Kafka and all the ecosystem built around it (Kafka Streams and Kafka Connect) are some of the key technologies as well as Apache Flink and Spark Streaming. Again, the main cloud vendors also own private solutions as MSK, Kinesis and Azure Event Hubs.

Data Science

Once all the data of the client are available in a centralised platform, the chances to extract insights from them increase exponentially. Techniques such as machine learning allow us to predict the behaviour of future events from historic stored data and also to calculate variables like the selling price of a product to increase benefits or the quantity of products to keep in stock to avoid breakages at lower cost. The group of these techniques of information is called data science and its results have multiple applications in every field of human knowledge.

Some of the libraries that are most used among data scientists to build models with the ability to predict and optimize variables are NumPy, sciti-learn, Tensorflow, Keras and PyTorch. Alternatively, Jupyter and Zeppelin are usually used to display the results of a data analysis. Frameworks such as MLFlow, KubeFlow or Sagemaker guarantee that the process of analysis, training and data publication is repeatable and scalable.

Business intelligence

The display of data is an essential area to obtain insigths in a company. It has been traditionally operated by Excels and manual procedures. However, the new requirements imposed by Big Data and the need to display information in real time has fostered the appearance of sophisticated tools for the development of powerful and attractive dashboards. Some of our favourites are Power BI, Tableau, Quicksight and Apache Superset. We have certified experts with wide experience working on them, developing reports and traning about the subject.

Consult us

In NEXT DIGITAL HUB we have professionals who are able to extrar the maximum value of your data. They are experienced in data lakes implantation projects, datawarehouses and business intelligente platforms. Contact us to understand your need and we will help you to lead your strategy of data governance to a new level.