Awesome Data Engineering

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Awesome Data Engineering by Mind Map: Awesome Data Engineering

1. Julia

2. Druid

3. Logit

4. Jupyter Notebook

4.1. Boosting / Ensembles

5. OLAP-specific

6. ML libs

6.1. High-level

6.1.1. Scikit-learn

6.1.2. Keras

6.1.3. Tensorforce

6.2. Low-level

6.2.1. Tensorflow

6.2.2. Theano

6.2.3. Caffe2

6.2.4. Torch

6.2.5. CNTK

7. OS / Shell / Environment

7.1. Linux

7.2. Bash

8. Programming Languages

8.1. Python

8.2. R

8.3. Scala

9. Working With Data

9.1. Mapreduce Systems

9.1.1. Hadoop

9.1.2. YT

9.2. RDBMS-like

9.2.1. Google BigQuery

9.2.2. Amazon Redshift

9.2.3. Yandex Clickhouse

9.2.4. CockroachDB

9.3. PostgreSQL-based

9.3.1. Greenplum

9.3.2. Citus

9.4. NoSQL

9.4.1. Elasticsearch

9.4.2. MongoDB

9.5. BI / Quering / Reports

9.5.1. Kibana

9.5.1.1. Tableau

9.5.2. Metabase

9.5.2.1. Superset

9.5.3. Plotly

9.5.4. Redash

9.6. ETL

9.6.1. Splunk

9.6.2. Talend

9.6.3. Singer.io

10. Machine Learning Areas

10.1. NLP

10.2. Picture

10.2.1. Style Transfer

10.2.2. Object Detection and Classification

10.2.2.1. Optical Character Recognition (OCR)

10.2.2.2. ImageNet / VGG16 / VGG19

10.3. Sound

10.3.1. Text-to-Speech

10.4. Speech recognition

11. Theory

11.1. Math

11.2. Stats

11.3. Algorithms

12. Machine Learning Methods

12.1. Neural Networks

12.1.1. Convolutional

12.1.2. Recurrent, LSTM

12.2. Support Vector Machine (SVM)

12.3. Decision Trees

12.4. Reinforcement Learning

13. DevOps

13.1. Continious Integragion

13.1.1. Gitlab CI

13.1.2. Travis CI

13.1.3. Drone

13.1.4. Teamcity

13.1.5. Jenkins

13.1.6. Buildbot

13.2. Amazon Web Services

13.3. Google Cloud Platform

13.4. Docker

13.5. Kubernetes

14. Minimal "Must have" example (could vary)

14.1. Bash

14.2. Scikit-learn

14.2.1. Jupyter Notebook

14.3. Keras

14.4. Docker

14.5. AWS or GCP

14.6. Gitlab CI