Detailed roadmap to become a big data engineer

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Detailed Roadmap

BIG DATA ENGINEER/ARCHITECT

to become a

Phase 1: Solidify Your Foundation

(6-12 months)

This phase focuses on building the essential skills that will

underpin your entire Big Data journey.

1 Programming

Python:

Core Python: Master data structures (lists, dictionaries, sets), algorithms, object-oriented programming (OOP), file handling, and exception handling.

Data Science Libraries: Become proficient in NumPy for numerical computing, Pandas for data manipulation and analysis, and Dask for parallel computing with larger-than-memory datasets.

API Development: Learn to build robust and efficient data APIs using frameworks like FastAPI or Flask.

Testing: Adopt testing practices early on with libraries like pytest to ensure code quality and reliability.

Java:

Core Java: Deep dive into JVM internals (garbage collection, memory management), concurrency (threads, synchronization), and performance optimization techniques.

Data Structures and Algorithms: Strengthen your understanding of fundamental data structures and algorithms for efficient data

processing.

Frameworks: Explore popular frameworks like Spring Boot for building enterprise-grade data applications.

Scala (Optional but Recommended):

Functional Programming: Grasp the core concepts of functional programming, which are essential for working with Spark

effectively.

Scala with Spark: Learn how to leverage Scala's conciseness and expressiveness for Spark development.

2 Database

SQL:

Advanced SQL: Go beyond basic CRUD operations.

Master window functions, common table expressions (CTEs), analytical functions, and query optimization techniques (indexing, query planning).

Database Design: Learn about database normalization, schema design, and data modeling best practices.

NoSQL:

Document Databases (MongoDB, Couchbase):

Understand schema design, indexing strategies, aggregation pipelines, and data modeling for document databases.

Key-value Stores (Redis, Memcached): Explore their use cases for caching, session management, and high-speed data retrieval.

Graph Databases (Neo4j, Amazon Neptune): Learn how

to model and query relationships in data using graph databases, and their applications in social networks, recommendation systems, and knowledge graphs.

Wide-Column Stores (Cassandra, HBase): Understand their distributed nature, data replication strategies,

consistency levels, and suitability for time-series data and high-write workloads.

3 Linux Proficiency

Command-Line Mastery: Become fluent in navigating the filesystem, managing processes, and using essential commandsfor file manipulation, system monitoring, and network

configuration

Shell Scripting: Automate repetitive tasks, manage data

pipelines, and improve your efficiency in a Linux environment

by writing shell scripts

System Administration Fundamentals: Gain a basic

understanding of user and permission management, service

management, and system monitoring tools

4 Data Warehousing and ETL Fundamentals

Data Warehousing Concepts: Learn about dimensional

modeling (star schema, snowflake schema), data partitioning,slowly changing dimensions (SCDs), and data warehouse

design best practices

ETL (Extract, Transform, Load): Understand the different stages

of ETL, data quality checks, and data validation techniques

Modern ETL Tools: Get hands-on experience with cloud-basedETL services like:

AWS Glue: A serverless ETL service that makes it easy toprepare and load data for analytics

Azure Data Factory: A visual ETL tool for creating andmanaging data pipelines in the Azure cloud

Google Cloud Dataflow: A fully managed service for batchand stream data processing

Database Practice: Install and work with different database

systems (MySQL, PostgreSQL, MongoDB, Cassandra) Createsample databases, write queries, and experiment with differentdata modeling techniques

Linux Practice: Set up a virtual machine with a Linux distribution(Ubuntu, CentOS) and practice using the command line and

"Data Warehousing for Business IntelligenceSpecialization" by University of Colorado Boulder(Solid introduction to data warehousing)

"SQL for Data Science" by UC Davis (Focuses on SQLfor data analysis)

edX:

"Introduction to Linux" by Linux Foundation (Greatstarting point for Linux)

Phase 2: Master the Big Data Ecosystem

(12-18 months)

This phase focuses on gaining in-depth knowledge and practical

experience with the key tools and technologies that form the backbone

of modern Big Data systems

1 Hadoop

Hadoop Distributed File System (HDFS):

Architecture: Understand HDFS's architecture, includingNameNode, DataNodes, and how data is distributed andreplicated across the cluster

File Formats: Learn about different file formats used in Hadoop,such as Avro, Parquet, and ORC, and their advantages in terms

of storage efficiency and query performance

Data Ingestion: Explore ways to ingest data into HDFS fromvarious sources (databases, filesystems, streaming platforms).YARN (Yet Another Resource Negotiator):

Resource Management: Understand how YARN managesresources (CPU, memory) in a Hadoop cluster and schedulesdifferent types of applications (MapReduce, Spark)

Capacity Scheduler: Learn how to configure YARN to allocateresources effectively and prioritize different applications

MapReduce:

Fundamentals: Grasp the core concepts of MapReduce(mapping, shuffling, reducing) and how it processes data inparallel across a cluster

MapReduce with Java: Learn to write MapReduce programs inJava to process data in HDFS

Data Warehousing with Hive: Understand how

Hive provides a SQL-like interface for querying

data stored in HDFS.

HiveQL: Master HiveQL, Hive's SQL dialect,

including data definition language (DDL) and data manipulation language (DML) statements.

Performance Optimization: Learn techniques like partitioning, bucketing, and indexing to optimize Hive queries for faster execution.

Hive with Spark: Explore how to use Spark as the execution engine for Hive queries to improve

performance.

HBase:

NoSQL on Hadoop: Understand how HBase

provides a low-latency, high-throughput NoSQL

database built on top of HDFS.

Data Modeling for HBase: Learn how to design

efficient data models for HBase, considering row keys, column families, and data access patterns HBase API: Learn how to interact with HBase using its Java API for data storage and retrieval.

2 Spark - The Powerhouse of Big Data

Processing

Spark Core:

Resilient Distributed Datasets (RDDs): Master the fundamental data structure in Spark, understanding its immutability, transformations, and actions.

Spark Execution Model: Learn how Spark executes jobs, including stages, tasks, and data shuffling.

Spark with Python (PySpark): Become proficient in using PySpark for data processing and analysis.

Spark SQL:

DataFrames and Datasets: Understand these higher-level abstractions in Spark that provide a more structured and optimized way to work with data.

SQL for Big Data: Learn how to use SQL to query and manipulate data within Spark.

Performance Optimization: Explore techniques like caching, data partitioning, and bucketing to optimize Spark SQL queries.

Spark Streaming:

Real-time Data Processing: Learn how to process real-time data streams using Spark Streaming, including windowing operations and stateful transformations.

Integration with Kafka: Build pipelines to ingest and process data from Kafka using Spark Streaming.

MLlib (Machine Learning Library):

Machine Learning at Scale: Explore Spark's machine learning library for building and deploying models on large datasets.

Algorithms: Learn about various machine learning algorithms available in MLlib, including classification, regression, clustering, and recommendation systems.

Actionable Steps:

Set up a Hadoop Cluster: Start with a single-node

cluster on your local machine using a virtual machine Then, explore multi-node clusters using cloud services (AWS EMR, Azure HDInsight, GCP Dataproc).

Work with Hadoop Tools: Practice using Hadoop

commands, write MapReduce jobs, create Hive tables, and explore HBase.

Spark Projects: Develop Spark applications using

PySpark for data processing, analysis, and machine learning tasks.

Online Courses:

Coursera:

"Big Data Specialization" by UC San Diego (Covers Hadoop, Spark, and other Big Data technologies)

Phase 3: Toolkit (12-18 months)

In this phase, you'll broaden your skillset by exploring essential tools and technologies that complement the core Big Data

ecosystem and enable you to build more sophisticated and

robust data solutions.

1 Real-time Streaming and Messaging

library for building real-time data processing applications, including windowing, aggregations, and joins.

Schema Registry: Understand the importance of schema management in Kafka and how to use a schema registry (e.g., Confluent Schema Registry) to ensure data

consistency.

Other Streaming Technologies:

Apache Pulsar: Explore this cloud-native distributed messaging and streaming platform, known for its scalability and multi-tenancy features.

Amazon Kinesis: Learn about this managed streaming service offered by AWS, including Kinesis Data

Streams, Kinesis Firehose, and Kinesis Analytics.

Azure Stream Analytics: Explore this real-time analytics service on Azure for processing high-volume data

2 Workflow Orchestration and Scheduling

Apache Airflow:

Data Pipeline Orchestration: Master Airflow for defining, scheduling, and monitoring complex data pipelines with dependencies and different task

types.

DAGs (Directed Acyclic Graphs): Learn how to define workflows as DAGs in Airflow, specifying tasks, dependencies, and schedules.

Operators and Sensors: Explore Airflow's built-in operators for common tasks (BashOperator,

PythonOperator, EmailOperator) and sensors for triggering tasks based on conditions.

Alternatives to Airflow:

Prefect: A modern dataflow orchestration tool with

a focus on ease of use and dynamic workflows.

Dagster: A data orchestrator designed for complex data pipelines and machine learning workflows.

3 Advanced Data Processing Engines

Apache Flink:

Stream Processing with Flink: Learn how to use Flink forstateful stream processing, handling high-volume data streamswith low latency

Flink SQL: Explore Flink's SQL capabilities for querying andprocessing both batch and streaming data

Use Cases: Understand Flink's applications in real-timeanalytics, fraud detection, and event-driven architectures

Presto:

Distributed SQL Query Engine: Learn how Presto enables fastinteractive queries on large datasets distributed across variousdata sources

Query Optimization: Understand Presto's query optimizer andtechniques for improving query performance

Connectors: Explore Presto's connectors to connect to differentdata sources (Hive, Cassandra, MySQL)

4 NoSQL Deep Dive

Advanced NoSQL Concepts:

Data Modeling Patterns: Explore different data modelingpatterns for NoSQL databases, including key-value, document,graph, and column-family

Consistency and Availability: Understand the trade-offsbetween consistency and availability in distributed databases(CAP theorem)

Database Administration: Learn about NoSQL databaseadministration tasks, including performance tuning, backup

Actionable Steps:

Kafka Cluster: Set up a Kafka cluster (using Confluent

Platform or a cloud-managed service) and practice

producing and consuming messages, using Kafka

Connect, and building streaming applications with Kafka Streams.

Airflow for Orchestration: Install Airflow and create data pipelines with different tasks (data extraction,

transformation, loading) and schedules.

Flink and Presto: Explore Flink and Presto by running

sample applications and queries on your data.

Phase 4: Learn Cloud and Modern Data Architectures (12-18 months)

This phase focuses on leveraging the power of cloud computing andadopting modern data architectures to build scalable, reliable, andcost-effective Big Data solutions

1 Cloud Platforms

Amazon Web Services (AWS):

Core Services: Gain a deep understanding of core AWS services, including:

EC2 (Elastic Compute Cloud): For provisioning virtual machines.

S3 (Simple Storage Service): For object storage.

IAM (Identity and Access Management): For security and access control.

VPC (Virtual Private Cloud): For networking.

Big Data Services: Master AWS services specifically designed for Big Data, such as:

EMR (Elastic MapReduce): For running Hadoop and Spark clusters.

Redshift: A cloud-based data warehouse.

Kinesis: For real-time data streaming.

Athena: For querying data in S3 using SQL.

Glue: For serverless ETL and data cataloging.

Lake Formation: For building and managing data lakes.

Microsoft Azure:

Core Services: Familiarize yourself with Azure's core services, including:

Virtual Machines: For provisioning virtual machines.

Blob Storage: For object storage.

Azure Active Directory: For identity and access management.

Virtual Network: For networking.

Big Data Services: Explore Azure's Big Data offerings:

HDInsight: For running Hadoop and Spark clusters.

Synapse Analytics: A unified analytics platform that brings together data warehousing, big data analytics, and data integration.

Data Lake Storage Gen2: For building data lakes.

Databricks: A managed Spark platform.

Stream Analytics: For real-time stream processing.

Data Factory: For visual ETL and data pipeline orchestration.

Google Cloud Platform (GCP):

Core Services: Learn GCP's fundamental services:

Compute Engine: For virtual machines.

Cloud Storage: For object storage.

Cloud IAM: For identity and access management.

Virtual Private Cloud: For networking.

Big Data Services: Dive into GCP's Big Data services:

Dataproc: For running Hadoop and Spark clusters.

BigQuery: A serverless, highly scalable data warehouse.

Pub/Sub: A real-time messaging service.

2 Modern Data Architectures

Data Lakes:

Data Lake Fundamentals: Understand the concepts of data lakes, including schema-on-read, data variety, and their suitability for diverse analytics and machine learning use cases.

Data Lake Design: Learn best practices for designing data lakes, including data organization, partitioning, security, and metadata management.

Data Lakehouse:

The Best of Both Worlds: Explore the data lakehouse architecture, which combines the flexibility of data lakes with the data

management and ACID properties of data warehouses.

Delta Lake: Learn how Delta Lake provides an open-source storage layer that brings reliability and ACID transactions to data lakes.

AWS Lambda: Learn how to use AWS Lambda to run code without

provisioning or managing servers.

Azure Functions: Explore Azure's serverless compute service for driven applications.

event-Google Cloud Functions: Learn about GCP's serverless compute

platform for running code in response to events.

Actionable Steps:

Cloud Platform Selection: Choose a cloud platform

(AWS, Azure, or GCP) and create a free tier account

to explore its services.

Hands-on Cloud Projects: Build data pipelines, deploy applications, and experiment with different Big Data services on your chosen cloud platform.

Data Lake Implementation: Design and implement a data lake using cloud storage and related services.

Serverless Data Processing: Build serverless data

processing functions using Lambda, Azure Functions,

or Google Cloud Functions.

Online Courses:

Coursera:

"Data Engineering with Google Cloud professional certification" by Google Cloud (Covers BigQuery, Dataflow, Dataproc)

"DP-203: Data Engineering on Microsoft Azure" (Prepares for the Azure Data Engineer Associate certification)

AWS Training:

"Big Data on AWS" (Comprehensive course

on AWS Big Data services)