Awesome Data Engineering

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Awesome Data Engineering by Mind Map: Awesome Data Engineering

1. Logit

2. Jupyter Notebook

2.1. Boosting / Ensembles

3. OLAP-specific

3.1. Druid

4. ML libs

4.1. High-level

4.1.1. Scikit-learn

4.1.2. Keras

4.1.3. Tensorforce

4.2. Low-level

4.2.1. Tensorflow

4.2.2. Theano

4.2.3. Caffe2

4.2.4. Torch

4.2.5. CNTK

5. OS / Shell / Environment

5.1. Linux

5.2. Bash

6. Programming Languages

6.1. Python

6.2. R

6.3. Julia

6.4. Scala

7. Working With Data

7.1. Mapreduce Systems

7.1.1. Hadoop

7.1.2. YT

7.2. RDBMS-like

7.2.1. Google BigQuery

7.2.2. Amazon Redshift

7.2.3. Yandex Clickhouse

7.2.4. CockroachDB

7.3. PostgreSQL-based

7.3.1. Greenplum

7.3.2. Citus

7.4. NoSQL

7.4.1. Elasticsearch

7.4.2. MongoDB

7.5. BI / Quering / Reports

7.5.1. Kibana

7.5.2. Tableau

7.5.3. Metabase

7.5.4. Redash

7.5.5. Superset

7.5.6. Plotly

7.6. ETL

7.6.1. Splunk

7.6.2. Talend

7.6.3. Singer.io

8. Machine Learning Areas

8.1. NLP

8.2. Picture

8.2.1. Style Transfer

8.2.2. Object Detection and Classification

8.2.2.1. Optical Character Recognition (OCR)

8.2.2.2. ImageNet / VGG16 / VGG19

8.3. Sound

8.3.1. Text-to-Speech

8.4. Speech recognition

9. Theory

9.1. Math

9.2. Stats

9.3. Algorithms

10. Machine Learning Methods

10.1. Neural Networks

10.1.1. Convolutional

10.1.2. Recurrent, LSTM

10.2. Support Vector Machine (SVM)

10.3. Decision Trees

10.4. Reinforcement Learning

11. DevOps

11.1. Continious Integragion

11.1.1. Gitlab CI

11.1.2. Travis CI

11.1.3. Drone

11.1.4. Teamcity

11.1.5. Jenkins

11.1.6. Buildbot

11.2. Amazon Web Services

11.3. Google Cloud Platform

11.4. Docker

11.5. Kubernetes

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

12.1. Bash

12.2. Jupyter Notebook

12.3. Scikit-learn

12.4. Keras

12.5. Docker

12.6. AWS or GCP

12.7. Gitlab CI