Category: Data Warehouse

  • Apache Nifi on Google Cloud Kubernetes Engine (GKE)

    Apache Nifi on Google Cloud Kubernetes Engine (GKE)

    Apache Nifi on GKE can be a good solution, if you want to have a low code solution for processing streaming data. If you set it up on GKE, a managed version of Kubernetes, you have a managed scalable environment and do not need to worry about handling the actual servers. Setup of the Apache […]

  • Data Infrastructure in the Cloud

    Data Infrastructure in the Cloud

    Having your data infrastructure in the cloud has become a real option for a lot of companies, especially since the big cloud providers have a lot of managed services available for a modern data architecture aside from just a database management system.

  • Google Cloud Data Engineer Exam Preparation

    Google Cloud Data Engineer Exam Preparation

    This is a little text with all the stuff that helped me prepare for the Google Cloud Data Engineer Exam. There are a lot of courses and resources, that help you in preparing for this. The following links helped me in preparation for my Google Data Engineer Exam. On Coursera there is are several courses […]

  • Analytics Platform: An Evolution from Data Lake

    Analytics Platform Having built a Data Lake for your company’s analytical needs, there soon will arise new use cases, that cannot be easily covered with the Data Lake architecture I covered in previous posts, like Apache HAWQ™: Building an easily accessable Data Lake. You will need to adapt or enhance your architecture to become more […]

  • Building a Productive Data Lake: How to keep three systems in sync

    Three Systems for save Development When you are building a productive Data Lake it is important to have at least three environments: Development: for development, where “everything” is allowed. Staging: for testing changes in a production like environment. Production: Running your tested and productive data applications With these different environments comes the need to keep […]

  • Apache HAWQ: Building an easily accessable Data Lake

    Data Lake vs Datawarehouse The Data Lake Architecture is an up and coming approach to making all data accessible through several methods, be that in real-time or batch analysis. This includes unstructured data as well as structured data. In this approach the data is stored on HDFS and made accessible by several tools, including: Apache […]

  • Apache HAWQ: Full SQL and MPP support on HDFS

    Pivotal ported their massively parallel processing (MPP) database Greenplum to Hadoop and made it open source as an incubating project at Apache, called Apache HAWQ. This bring together full ANSI SQL with MPP capabilities and Hadoop integration. The integration in an existing Hadoop installation is easy, as you can integrate all existing data via external […]

  • Big Data and Data Warehouse Architecture

    Further development and new additions to the Hadoop framework, such as Stinger from HortonWorks or Impala from Cloudera try to bridge the gap between traditional EDWH architectures and big data architectures. Especially Stinger.next initiative with the goal of speeding up Hive and delivering SQL 2011 standard to use on Map / Reduce Hadoop clusters makes […]

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