Awesome Data Engineering

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Awesome Data Engineering by Mind Map: Awesome Data Engineering

1. ML libs

1.1. High-level

1.1.1. Scikit-learn

1.1.2. Keras

1.1.3. Tensorforce

1.2. Low-level

1.2.1. Tensorflow

1.2.2. Theano

1.2.3. Caffe2

1.2.4. Torch

1.2.5. CNTK

2. OS / Shell / Environment

2.1. Linux

2.2. Bash

2.3. Jupyter Notebook

3. Programming Languages

3.1. Python

3.2. R

3.3. Julia

3.4. Scala

4. Working With Data

4.1. Mapreduce Systems

4.1.1. Hadoop

4.1.2. YT

4.2. RDBMS-like

4.2.1. Google BigQuery

4.2.2. Amazon Redshift

4.2.3. Yandex Clickhouse

4.2.4. PostgreSQL-based

4.2.4.1. Greenplum

4.2.4.2. Citus

4.2.5. CockroachDB

4.3. OLAP-specific

4.3.1. Druid

4.4. NoSQL

4.4.1. Elasticsearch

4.4.2. MongoDB

4.5. BI / Quering / Reports

4.5.1. Kibana

4.5.2. Tableau

4.5.3. Metabase

4.5.4. Redash

4.5.5. Superset

4.5.6. Plotly

4.6. ETL

4.6.1. Splunk

4.6.2. Talend

4.6.3. Singer.io

5. Machine Learning Areas

5.1. NLP

5.2. Picture

5.2.1. Style Transfer

5.2.2. Object Detection and Classification

5.2.2.1. Optical Character Recognition (OCR)

5.2.2.2. ImageNet / VGG16 / VGG19

5.3. Sound

5.3.1. Text-to-Speech

5.3.2. Speech recognition

6. Theory

6.1. Math

6.2. Stats

6.3. Algorithms

7. Machine Learning Methods

7.1. Neural Networks

7.1.1. Convolutional

7.1.2. Recurrent, LSTM

7.2. Support Vector Machine (SVM)

7.3. Logit

7.4. Decision Trees

7.5. Boosting / Ensembles

7.6. Reinforcement Learning

8. DevOps

8.1. Continious Integragion

8.1.1. Gitlab CI

8.1.2. Travis CI

8.1.3. Drone

8.1.4. Teamcity

8.1.5. Jenkins

8.1.6. Buildbot

8.2. Amazon Web Services

8.3. Google Cloud Platform

8.4. Docker

8.5. Kubernetes

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

9.1. Bash

9.2. Jupyter Notebook

9.3. Scikit-learn

9.4. Keras

9.5. Docker

9.6. AWS or GCP

9.7. Gitlab CI