Skip to main content

Streamsets Data Collector 1.6.0.0 has been released!

The release 1.6.0.0 of the Streamsets Data Collector has been released on September 1st. This release comes with an incredible number of new features. Here are some of the most interesting:

  • JDBC Lookup processor: it can perform lookups in a database table through a JDBC connection and then you can use the values to enrich records.
  • JDBC Tee processor: it can write data to a database table through a JDBC connection, and then you can pass generated database column values to fields. 
  • Support for reading data from paginated webpages through the HTTP origin.
  • Support for Apache Kafka 0.10 and ElasticSearch 2.3.5.
  • Enterprise security in the MongoDB origin and destination including SSL and login credentials.
  • Whole File Data format: to move entire files from an origin system (Amazon S3 or Directory) to a destination system (Amazon S3, HDFS, Local File System or MapR FS). Using the whole file data format, you can transfer any type of file. 
And many more.
Furthermore, it is now possible to download a smaller installer package (TGZ or RPM) and then install manually only the individual packages you really need. Here is the sequence of commands for the core TGZ archive to download it and installing the required stages:

# extract the tar file
$ tar xvzf streamsets-datacollector-core-1.6.0.0.tgz


$ cd streamsets-datacollector-core-1.6.0.0
# list all downloadable stage libraries
$ ./bin/streamsets stagelibs -list

# install stage libraries as required
$ ./bin/streamsets stagelibs -install=<stageid1>,<stageid2> 


Enjoy it!

Comments

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You