Home
Disclaimer
Required Tools
1.
Big Data Overview
1.1.
Introduction
1.2.
Job Opportunities
1.3.
What is Data?
1.4.
How does it help?
1.5.
Types of Data
1.6.
The Big V's
1.6.1.
Variety
1.6.2.
Volume
1.6.3.
Velocity
1.6.4.
Veracity
1.6.5.
Other V's
1.7.
Trending Technologies
1.8.
Big Data Concerns
1.9.
Big Data Challenges
1.10.
Data Integration
1.11.
Scaling
1.12.
Cap Theorem
1.13.
Optimistic Concurrency
1.14.
Eventual Consistency
1.15.
Concurrent vs Parallel
1.16.
GPL
1.17.
DSL
1.18.
Big Data Tools
1.19.
NO Sql Databases
1.20.
Learning Big Data means?
2.
Developer Tools
2.1.
Poetry
2.2.
UV
2.3.
Other Python Tools
2.4.
Duck DB
2.5.
JQ
3.
Data Format
3.1.
Common Data Formats
3.2.
JSON
3.3.
Parquet
3.4.
Arrow
3.5.
Delta
4.
Protocol
4.1.
HTTP
4.2.
Monolithic Architecture
4.3.
Statefulness
4.4.
Microservices
4.5.
Statelessness
4.6.
Idempotency
4.7.
REST API
4.8.
API Performance
4.9.
API in Big Data world
5.
Advanced Python
5.1.
Functional Programming Concepts
5.2.
Code Quality & Safety
5.3.
Decorator
5.4.
Serialization Deserialization
5.5.
Python Classes
5.6.
Unit Testing
5.7.
Data Frames
5.8.
Error Handling
5.9.
Logging
5.10.
Flask
5.10.1.
Setup
5.10.2.
Flask Demo
5.10.3.
Flask Demo-01
5.10.4.
Flask Demo-02
5.10.5.
Flask Demo-03
5.10.6.
Flask Demo-04
5.10.7.
Flask Demo-05
5.10.8.
API Testing
5.10.9.
Flask Demo Testing
6.
NO SQL
6.1.
Types of No SQL
6.2.
Redis
6.2.1.
Terms to know
6.2.2.
Redis - (Rdbms) Mysql
6.2.3.
Redis Cache Demo
6.2.4.
Use Cases
6.2.5.
Databases
6.2.6.
Data Structures
6.2.6.1.
Strings
6.2.6.2.
List
6.2.6.3.
Set
6.2.6.4.
Hash
6.2.6.5.
Pub Sub
6.2.6.6.
Geospatial Index
6.2.6.7.
Redis Python
6.2.7.
Redis JSON
6.2.8.
Redis Search
6.2.9.
Persistence
6.2.10.
Timeseries
6.3.
Neo4J
6.3.1.
Neo4j Terms
6.3.2.
Software
6.3.3.
Neo4j Components
6.3.4.
Hello World
6.3.5.
Examples
6.3.5.1.
Mysql Neo4j
6.3.5.2.
Sample Transactions
6.3.5.3.
Sample
6.3.5.4.
Create Nodes
6.3.5.5.
Update Nodes
6.3.5.6.
Relation
6.3.5.7.
Putting it all-together
6.3.5.8.
Commonly Used Functions
6.3.5.9.
Data Profiling
6.3.5.10.
Queries
6.3.5.11.
Load CSV into Neo4J
6.3.5.12.
Fraud Detection
6.3.5.13.
Python Scripts
6.3.6.
Certification
6.4.
Mongodb
6.4.1.
Sample JSON
6.4.2.
Introduction
6.4.3.
Software
6.4.4.
Mongodb Best Practices
6.4.5.
Mongodb Commands
6.4.6.
Insert Document
6.4.7.
Querying Mongodb
6.4.8.
Update & Remove
6.4.9.
Import
6.4.10.
Logical Operators
6.4.11.
Data Types
6.4.12.
Operators
6.4.13.
Aggregation Pipeline
6.4.14.
Further Reading
6.4.15.
Fun Task
6.4.15.1.
Sample
7.
Linux & Tools
7.1.
Overview
7.2.
CSV SQL
7.3.
Linux Commands 01
7.4.
Linux Commands 02
7.5.
AWK
7.6.
CSV Grep
7.7.
CSV Kit
8.
Tools
8.1.
CICD Intro
8.2.
CICD Tools
8.2.1.
CI Yaml
8.2.2.
CD Yaml
8.3.
Containers
8.3.1.
VMs or Containers
8.3.2.
What Container does
8.3.3.
Podman
8.3.4.
Podman Examples
9.
Cloud Everywhere
9.1.
Types of Cloud Services
9.2.
Challenges of Cloud Computing
9.3.
High Availability
9.4.
Azure Cloud
9.4.1.
Services
9.4.2.
Storages
9.4.3.
Demo
9.5.
Terraform
10.
Data Engineering
10.1.
Batch vs Streaming
10.2.
Kafka
10.2.1.
Kafka use cases
10.2.2.
Kafka Software
10.2.3.
Python Scripts
10.2.4.
Different types of streaming
10.3.
Quality & Governance
10.4.
Medallion Architecture
10.5.
Data Engineering Model
10.6.
Data Mesh
11.
Industry Trends
11.1.
Roadmap Data Engineer
11.2.
Notebooks vs IDE
11.3.
Good Reads
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The Big V’s of Big Data
Variety
Volume
Velocity
Veracity
Other V’s
#bigv
#bigdata
Ver 5.5.9