DataScientists: a blog about everything data related.
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Bringing machine learning models into production
Developing and bringing machine learning models into production is a task with a lot of challenges. These include model and attribute selection, dealing with missing values, normalization and others. Finding a workflow that puts all the gears, from data preprocessing and analysis over building models and selecting the best performing one to serving the model…
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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…
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AVRO schema generation with reusable fields
Why use AVRO and AVRO Schema? There are several serialized file formats out there, so chosing the one most suited for your needs is crucial. This blog entry will not compare them, but it will just point out some advantages of AVRO and AVRO Schema for an Apache Hadoop ™ based system. Avro schema can…
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Plumber: Getting R ready for production environments?
R Project and Production Running R Project in production is a controversially discussed topic, as is everything concerning R vs Python. Lately there have been some additions to the R Project, that made me look into this again. Researching R and its usage in production environments I came across several packages / project, that can…
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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…
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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…
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Apache AVRO: Data format for evolution of data
Flexible Data Format: Apache AVRO Apache AVRO is a data serialization format. It comes with an data definition format that is easy to understand. With the possibility to add optional fields there is a solution for evolution of the schemas for the data. Defining a Schema Defining a schema in Apache AVRO is quite easy,…
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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…
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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…
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Apache Zeppelin: Use with remote Spark cluster and Yarn
Apache Zeppelin is pretty usefull for interactive programming using the web browser. It even comes with its own installation of Apache Spark. For further information you can check my earlier post. But the real power in using Spark with Zeppelin lies in its easy way to connect it to your existing Spark cluster using YARN.…
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