Hadoop-Airflow Analytics.
First setup conda with Python 3.6, then:
git clone [email protected]:mblomdahl/o3.git
cd o3/
wget -P resources/ http://apache.mirrors.spacedump.net/hadoop/common/hadoop-2.9.2/hadoop-2.9.2.tar.gz
wget -P resources/ http://apache.mirrors.spacedump.net/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
wget -P resources/ http://apache.mirrors.spacedump.net/hive/hive-2.3.4/apache-hive-2.3.4-bin.tar.gz
wget -P resources/ https://repo.continuum.io/archive/Anaconda3-2018.12-Linux-x86_64.sh
conda env create --name o3 python=3.6 -f environment-macos.yml
conda activate o3; pip install -e .
export AIRFLOW_HOME=$(pwd)/airflow_home
# Checkout your secret enterprise DAGs into `prod-dags` root dir.
git clone ssh://[email protected]:7999/~myself/prod-dags.git prod-dags
airflow initdb
# Update newly-generated airflow.cfg in AIRFLOW_HOME by setting `dags_folder=$(pwd)/airflow_home/dags`.
airflow webserver -p 8080
These commands needs to be executed on the target platform. The output file can then replace the
corresponding file in this repo, i.e. environment-linux.yml and environment-macos.yml:
conda create --name o3 --yes python=3.6
conda install --name o3 -c conda-forge --yes psycopg2 hdfs3 airflow libhdfs3=2.3.0=1 ansible netaddr \
ipython pandas fastavro pyhive jupyter xlrd matplotlib paramiko bcrypt requests-futures
conda activate o3; pip install -e .
conda env export --name o3 > environment-<platform>.yml
ansible-playbook -i inventories/<inventory>.ini provision.yml
During an Ansible provisioning a Jupyter Notebook will be deployed on port 8888 of the provisioned server. It can also be executed in a development environment, like so:
conda activate o3; jupyter notebook --notebook-dir=notebooks
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