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Data forecasting

Data forecasting is a process of estimating the future based on historical values. It is described by time series, which is simply a series of time dependent data points. We usually forecast different costs or sales over time. We can try to predict weather conditions or model stock changes. Basically look at any process that can be described as time dependent with certain time interval (hourly, daily, monthly…).

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Excel formatting with R

Many times there is a demand from end users to deliver results in a form of Excel files. The bigger and more complex the files grow, the more difficult it is to properly interpret them. This is when nice formatting can help you out.

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XGBoost

XGBoost or in long version Extreme Gradient Boosting got recently very popular, especially on Kaggle competitions. It proved to outperform many other algorithms on tasks such as classification and regression. I used it few times as well and that’s why I decided to take a closer look into XGBoost to see how it works.

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Apache Beam JDBC

With Apache Beam we can connect to different databases – HBase, Cassandra, MongoDB using specific Beam APIs. We also have a JdbcIO for JDBC connections. Here I show how to connect with MSSQL database using Beam and do some data importing and exporting in Kerberised environment.

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Apache Beam and HBase

HBase is a NoSql database, which allows you to store data in many different formats (like pictures, pdfs, textfiles and others) and gives the ability to do fast and efficient data lookups. HBase has two APIs to chose from – Java API and HBase Shell. We can also connect HBase with some different tools like Hive or Phoenix and use SQL. HBase also integrates with Apache Beam via HBaseIO transform.

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Apache Beam – getting started

Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream processing. It is a processing tool which allows you to create data pipelines in Java or Python without specifying on which engine the code will run. So the same code can be run on MapReduce, Spark, Flink, Apex or some other engine.

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Machine Learning with Java libraries

When I think about Machine Learning there comes R and Python into my mind. There’s a nice set of ML libraries and packages that can be used to perform analysis or visualize data in both of those languages. But when it comes to Java ML libraries there aren’t that many. Of course there are nice Java frameworks, but they are mostly designed in such a way that you don’t actually do the coding. So how can a Java programmer easily incorporate ML into their application? I used 2 libraries which allowed me to do exactly that.

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Spark AI summit 2018 in San Francisco

Last week I attended the Spark AI summit in San Francisco. I was really curious to check what’s the difference between conferences in Europe and in the US. I must say the one in SF was definitely bigger. There were 11 different session tracks to attend and around 4000 people. In terms of organisation and content it was as good as the Spark summit 2016 in Brussels (well, minus the chocolate ;), which I also attended.

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