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DATA ENGINEER 저자: Mind Map: DATA ENGINEER

1. 6. Relational Database

1.1. MY SQL

1.2. PostgreSQL

1.3. Maria DB

1.4. Amazon Aurora

2. 5. Database Fundamentals

2.1. SQL

2.2. Normalisation

2.3. Acid Transactions

2.4. Oracle database

2.5. Microsoft SQL server

2.6. CAP theorem

2.7. OLTP vs OLAP

2.8. Horizontal vs vertical scaling

2.9. Dimensional modelling

3. 1. CS fundamentals

3.1. How does the computer work?

3.2. How does the Internet work?

3.3. Git — Version control

3.4. Basic Terminal usage

3.5. APIs

3.6. Data structures & algorithms

3.7. REST

3.8. Structured vsunstructured data

3.9. Serialisation

3.10. Linux

3.10.1. CLI

3.10.2. Vim

3.10.3. Shell scripting

3.10.4. Cronjob

3.11. Math & statistics

4. 2. Learn about Operating Systems

4.1. Linux

4.2. Mac OS

4.3. Windows

5. 3. Learn a programming Language

5.1. Jave

5.2. C++

5.3. Python

5.4. Scala

5.5. Go

6. 4. Testing

6.1. Unit testing

6.2. Integration testing

6.3. Functional testing

7. 14. Monitoring data Pipelines

7.1. Prometheus

7.2. Datadog

7.3. Sentry

7.4. StatsD

8. 13. Workflow Scheduling

8.1. Apache Airflow

8.2. Google Composer

8.3. Apache Oozie

8.4. Luigi

9. 11. Data processing

9.1. Batch

9.1.1. Apache Pig

9.1.2. Apache Arrow

9.1.3. data build tool

9.2. Streaming

9.2.1. Apache Kafka

9.2.2. Apache Storm

9.2.3. Apache Samza

9.2.4. Amazon Kinesis

9.3. Hybrid

9.3.1. Apache Spark

9.3.2. Apache Beam

9.3.3. Apache Flink

9.3.4. Apache NiFi

10. 10. Cluster computing Fundamentals

10.1. Apache Hadoop

10.2. HDFS

10.3. MapReduce

10.4. Lambda & Kappa 2 architectures

10.5. Managed Hadoop

10.5.1. Amazon EMR

10.5.2. Google Dataproc

10.5.3. Azure Data Lake

11. 9. Object Storage

11.1. AWS S3

11.2. Azure blob storage

11.3. Google cloud storage

12. 7. Non relational Database

12.1. Document

12.1.1. MongoDB

12.1.2. Apache CouchDB

12.1.3. Elasticsearch

12.1.4. Azure CosmosDB

12.2. Wide Coloumn

12.2.1. Apache Cassandra

12.2.2. Apache HBase

12.2.3. Google Bigtable

12.3. Graph

12.3.1. Neo4j

12.3.2. Amazon Neptune

12.4. Key - Value

12.4.1. Redis

12.4.2. Memcached

12.4.3. Amazon DynamoDB

13. 12. Messaging

13.1. Google PubSub

13.2. Amazon SNS & SQS

13.3. Azure Service Bus

13.4. RabbitMQ

13.5. Apache ActiveMQ

14. 8.Data warehouses

14.1. Snowflake

14.2. Presto

14.3. Apache Hive

14.4. Apache Impala

14.5. Amazon Redshift

14.6. Google BigQuery

14.7. Azure Synapse

14.8. ClickHouse

15. 16. Infrastructure as code

15.1. Containers

15.1.1. Docker

15.1.2. LXC

15.2. Containerorcehstrain

15.2.1. Kubernetes

15.2.2. Docker Swarm

15.2.3. Apache Mesos

15.2.4. GKE

15.3. Infrastructure Provisioning

15.3.1. Terraform

15.3.2. Pulumi

15.3.3. AWS CDK

16. 17. CI/CD

16.1. Github Actions

16.2. Jenkins

17. 15. Networking

17.1. Protocols

17.1.1. HTTP/HTTPS

17.1.2. TCP

17.1.3. SSH

17.1.4. IP

17.1.5. DNS

17.2. Firewalls

17.3. VPN

17.4. VPC

18. 18. Identity and access management

18.1. Active Directory

18.2. Azure Active Directory

19. 19. Data security & privacy

19.1. Legal compliance

19.2. Encryption

19.3. Key management

19.4. Data governance & integrity