Data Engineering

You already have a vision of your Data Intelligence platform, but need expert help or capacity to build and operationalize this vision. Data Engineering involves all the work and infrastructure needed to allow data scientists and analysts to do their jobs. 

In most organizations, data exists in irregular formats, siloed away from complementary data and the people that can make use of it. Data engineering is required to normalize and move that data in order to make it useful.

Computer vision (CV) may be used to convert photographic data to textual or numeric data. Natural language processing (NLP) may be used to convert free-form text data into structured data. 

ETL (Extract, Transform and Load) technologies may be used to normalize data formats – big data platforms like Apache Hadoop, Teradata, Apache Spark, Apache Kafka, and many others may be used to process large quantities of data and move it to an accessible location such as a centralized Data Lake. From there, human data scientists and analysts, as well as BI systems and dashboards, can work with it to provide insights to help inform your business decisions.

Data Engineering services are involved in all of our solutions, but here we can cut to the chase and you can tell us the specific areas of support you require.

These services include:

  • Data Analytics Automation

  • Big Data Processing Platform Architecture and Implementation (Cloudera, Hortonworks, IBM, Teradata, etc)

  • Hadoop Ecosystem Architecture and Development (Hive, Sqoop, Pig, Spark, Storm, Flink, Apex, Kafka, HDFS, MapReduce, HBase, Cassandra, YARN, etc.)

  • Data Processing Automation (Jenkins, Oozie, Azkaban, Talend, etc.)

  • Internet of Things (IoT) implementation and integration

We Believe

We believe that your analysts do their best work when the data are "just there" when they need it. And that requires engineering.