Awesome Data Engineering

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Awesome Data Engineering by Mind Map: Awesome Data Engineering

1. Druid

2. Logit

3. Jupyter Notebook

3.1. Boosting / Ensembles

4. OLAP-specific

5. ML libs

5.1. High-level

5.1.1. Scikit-learn

5.1.2. Keras

5.1.3. Tensorforce

5.2. Low-level

5.2.1. Tensorflow

5.2.2. Theano

5.2.3. Caffe2

5.2.4. Torch

5.2.5. CNTK

6. OS / Shell / Environment

6.1. Linux

6.2. Bash

7. Programming Languages

7.1. Python

7.2. R

7.3. Julia

7.4. Scala

8. Working With Data

8.1. Mapreduce Systems

8.1.1. Hadoop

8.1.2. YT

8.2. RDBMS-like

8.2.1. Google BigQuery

8.2.2. Amazon Redshift

8.2.3. Yandex Clickhouse

8.2.4. CockroachDB

8.3. PostgreSQL-based

8.3.1. Greenplum

8.3.2. Citus

8.4. NoSQL

8.4.1. Elasticsearch

8.4.2. MongoDB

8.5. BI / Quering / Reports

8.5.1. Kibana

8.5.1.1. Tableau

8.5.2. Metabase

8.5.2.1. Superset

8.5.3. Redash

8.5.4. Plotly

8.6. ETL

8.6.1. Splunk

8.6.2. Talend

8.6.3. Singer.io

9. Machine Learning Areas

9.1. NLP

9.2. Picture

9.2.1. Style Transfer

9.2.2. Object Detection and Classification

9.2.2.1. Optical Character Recognition (OCR)

9.2.2.2. ImageNet / VGG16 / VGG19

9.3. Sound

9.3.1. Text-to-Speech

9.4. Speech recognition

10. Theory

10.1. Math

10.2. Stats

10.3. Algorithms

11. Machine Learning Methods

11.1. Neural Networks

11.1.1. Convolutional

11.1.2. Recurrent, LSTM

11.2. Support Vector Machine (SVM)

11.3. Decision Trees

11.4. Reinforcement Learning

12. DevOps

12.1. Continious Integragion

12.1.1. Gitlab CI

12.1.2. Travis CI

12.1.3. Drone

12.1.4. Teamcity

12.1.5. Jenkins

12.1.6. Buildbot

12.2. Amazon Web Services

12.3. Google Cloud Platform

12.4. Docker

12.5. Kubernetes

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

13.1. Bash

13.2. Scikit-learn

13.2.1. Jupyter Notebook

13.3. Keras

13.4. Docker

13.5. AWS or GCP

13.6. Gitlab CI