In today’s fast-paced world, data is the new oil, and businesses that know how to harness data effectively have a massive competitive advantage.

So I asked myself —

•  Why am I not learning Big Data and integrating it into my architecture designs?
•  Why are Data Engineers driving AI/ML pipelines and data-driven business models, but architects still limited to application development?
•  How can I design applications that are AI/ML-powered and can leverage Big Data?

This realization sparked my journey to learn Big Data as an Application Architect.

What is Big Data ?

Bid data refers to a massive volume of inforamtion (data) generated every second from various sources like: social media, Business transaction, sensors, websites and apps. This data is so large, fast and complex that traditional computer can’s process it. so companies use Big Data technologies. However IBM provies a formal definition of Big data. In modern Big Data is characterised by 5V’s is considered to be Big Data.

1. Volume (Size): Data that is so huge that it cannot be stored or processed on a single conventional system is considered as Big Data. examples : Massive data generated daily from Amazon orders, YouTube videos.

2. Velocity (Speed) : Real-time data generation such as stocks prices, GPS tracking. the speed at which data is coming in System should be ablet occomodate the new huge volume of data to generate so insights for business. 

3. Variety (Type): Data can be coming in all the different format and we have to handle it. It can be Structured, Unstructured, Semi-Structured such as video, images, logs 

4. Veracity (Accuracty): Veracity means how accurate, clean, and reliable the data is. If data contains errors, duplicates, or fake information, businesses may make wrong decisions. Cleaning and processing data to remove inaccuracies is critical. Such as avoid fake new, dirty data. 

5. Value (Business Benefit) : Value is the most important V of Big Data. It refers to the business benefit gained from processing Big Data. If you collect huge data but can’t convert it into business insights, predictions, or revenue — the data is useless. for example : Netflix Recommendations, Uber Pricing.

Why Is There a Need to Learn a New Technology Stack to Handle Big Data?

In the modern era, data has become the new oil for businesses. Every second, millions of events, transactions, videos, logs, and user interactions are generated across the globe. This flood of data, known as Big Data, has completely transformed the way companies operate.

However, traditional technology stacks like RDBMS (Relational Databases), monolithic architectures, and batch processing systems are struggling to cope with the demands of Big Data. As a result, businesses are shifting towards modern Big Data stacks to handle massive data volume, real-time processing, and derive meaningful insights.

Traditional Systems Can’t Handle Massive Data Volumes (Volume Problem), to understand this we have to understand the Monolithic Systems vs Distributed Systems.

Monolithic Systems vs Distributed Systems in Big Data

Why Monolithic Systems Fail in Big Data

A monolithic system is a single-tiered software architecture where all components (database, server, processing logic) are tightly coupled. This approach works fine for small datasets but fails miserably when dealing with Big Data due to:

• Limited Resources: Monolithic systems operate on a single server, limiting CPU, memory, and processing power.
• Single Point of Failure: If the server crashes, the entire system goes down.
• Scalability Challenges: Scaling monolithic systems means adding more power to the single server (vertical scaling), which is costly and inefficient.
• Batch Processing: Monolithic systems typically process data in batches, resulting in delayed insights

Why Distributed Systems Work for Big Data

A distributed system, on the other hand, splits data and processing tasks across multiple servers, allowing for:

• Horizontal Scaling: Add more servers to process large datasets.
• Fault Tolerance: If one server fails, others take over.
• Parallel Processing: Distributed systems can process millions of data points in real-time.
• Cost Efficiency: Uses commodity hardware, reducing costs.

Overview of Hadooop

In the early 2000s, companies struggled to process massive volumes of data (Big Data). Traditional systems couldn’t handle the growing Volume, Variety, and Velocity of data. That’s when Hadoop emerged as a game-changer.

What is Hadoop?
Hadoop is an open-source framework designed to store, process, and analyze large datasets across distributed systems using simple programming models. It became the backbone of Big Data processing.

The Evolution of Hadoop:

• Inspired by Google’s GFS (Google File System) and MapReduce in 2003.
• Yahoo built Hadoop in 2006 to solve Big Data challenges.
• Today, giants like Facebook, Twitter, Netflix, and Uber heavily rely on Hadoop.

Three Core Components of Hadoop:

1. HDFS (Hadoop Distributed File System):
   • Stores massive data across multiple machines.
2. YARN (Yet Another Resource Negotiator):
   • Manages computing resources across the cluster.
3. MapReduce:
   • Processes large data by splitting it into smaller tasks and aggregating results.

Hadoop Ecosystem Technologies:
Hadoop’s power expanded with various tools:

• Hive: SQL-like querying for Big Data.
• HBase: NoSQL database for real-time read/write.
• Sqoop: A command-line interface application for efficiently transferring bulk data between relational databases and Hadoop
• Pig: Data transformation with scripting.
• Oozie: Workflow management.

Challenges with Hadoop: As a file-intensive system, MapReduce can be a difficult tool to utilize for complex jobs, such as interactive analytical tasks. MapReduce functions also need to be written in Java and can require a steep learning curve. The MapReduce ecosystem is quite large, with many components for different functions that can make it difficult to determine what tools to use. 

• Real-time processing is limited (Spark solved this).
• High infrastructure costs (Cloud solutions minimize this).
• Data governance and security still need improvement.

Cloud vs Its Advantages

In the era of explosive data growth, organizations face a critical decision: should they manage their big data infrastructure on-premise or leverage the cloud? Both approaches have their merits, but the cloud’s scalability and flexibility are increasingly making it the preferred choice for big data workloads.

Cloud Types for Big Data:

For big data, the three primary cloud models are:

• Infrastructure as a Service (IaaS): Provides virtualized computing resources, such as servers, storage, and networking. This gives organizations control over the underlying infrastructure while leveraging the cloud’s scalability.  
• Platform as a Service (PaaS): Offers a development and deployment environment, including operating systems, databases, and middleware. This simplifies application development and management.  
• Software as a Service (SaaS): Delivers ready-to-use applications over the internet, such as data analytics platforms and business intelligence tools. This eliminates the need for software installation and maintenance.  

Advantages of Cloud for Big Data:

• Cost Optimization: Pay-as-you-go pricing and elastic scalability minimize unnecessary expenses.   
• Improved Agility: Rapid provisioning and deployment accelerate time-to-market.   
• Enhanced Collaboration: Cloud-based platforms facilitate data sharing and collaboration among teams.  
• Increased Innovation: Access to advanced analytics tools and services empowers organizations to gain deeper insights and drive innovation.  
• Global Accessibility: Cloud services can be accessed from anywhere with an internet connection.

What is Apache spark ?

Apache Spark is a powerful, open-source, distributed processing system designed for big data workloads. It’s known for its speed, ease of use, and versatility.

Problems which Apache Spark Solves:

1. Slow Batch Processing: Traditional systems like Hadoop MapReduce can be slow, especially for iterative tasks. Spark’s in-memory processing dramatically speeds up these workloads.  
2. Complex Data Pipelines: Spark simplifies the creation of complex data pipelines by providing a unified platform for various data processing tasks.  
3. Real-time Data Processing: Spark Streaming enables real-time analysis of streaming data, which is essential for applications like fraud detection and online monitoring.  
4. Machine Learning at Scale: Spark’s MLlib library provides a scalable machine learning platform for building and deploying models on large datasets.  
5. Graph Processing: GraphX, Spark’s graph processing library, allows for efficient analysis of complex relationships in data.

Note: It’s more accurate to say that Spark complements Hadoop, and in some cases, provides a more efficient alternative to parts of it. so we can say Apache spark is not a replement of Hadoop. spark can run on top of HDFS, leveraging Hadoop’s distributed storage. 

Database vs  data warehouse vs and data lake

Successful organizations derive business value from their data. One of the first steps towards a successful big data strategy is choosing the underlying technology for storing, searching, analyzing, and reporting data. Here, we’ll cover common questions—what is a database, a data lake, or a data warehouse? What are the differences between them, and which should you choose?

1. Database: A database is a structured collection of data that is stored and managed electronically. It allows for efficient storage, retrieval, modification, and management of data. Databases are used to store information in an organized manner, typically using tables, and are accessed through database management systems (DBMS).
2. Data Warehouse: A Data Warehouse is a centralized repository that stores structured data from multiple sources, optimized for querying and reporting. It is used for business intelligence and analytics.
3. Data lake : A Data Lake is a large, unstructured repository that stores raw data in its native format, including structured, semi-structured, and unstructured data, allowing for flexibility in data processing and analysis.

The main differences between Data Warehouse and Data Lake in terms of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are:

1. Data Warehouse (ETL):
   • ETL Process: Data is extracted from various sources, transformed (cleaned, filtered, and processed), and then loaded into the data warehouse.
   • Purpose: Data is structured and optimized for reporting and analytics, so the transformation happens before loading to ensure consistency and quality.
2. Data Lake (ELT):
   • ELT Process: Data is extracted from sources, loaded in its raw form into the data lake, and then transformations are applied later when needed.
   • Purpose: Data is stored in its native format (structured, semi-structured, or unstructured), allowing more flexibility for future analysis, as transformations are performed after loading when specific insights are required.

In short, Data Warehouses typically use ETL, while Data Lakes often use ELT to allow more flexibility in how data is processed and analyzed.

Data Engineering Flow: Building the Backbone of Data Systems

In the world of data-driven decision-making, the importance of a robust data engineering flow cannot be overstated. Data engineers are responsible for designing, building, and maintaining the architecture that makes data accessible, reliable, and usable. The data engineering flow is a process that converts raw data into actionable insights, enabling businesses to harness the full potential of their data.

Here’s a look at the typical data engineering flow:

1. Data Collection: The first step involves gathering data from a variety of sources, such as databases, APIs, IoT devices, web scraping, and more. This raw data may come in various formats, including structured, semi-structured, or unstructured data.
2. Data Ingestion: Once collected, data is ingested into a storage system. This can happen in real-time (streaming data) or batch processing. Tools like Apache Kafka, AWS Kinesis, or Google Pub/Sub are commonly used to handle real-time data streaming, while batch processes can be handled by tools like Apache NiFi or Airflow.
3. Data Storage: The ingested data is stored in centralized repositories. These may be Data Lakes for raw data (Hadoop, Amazon S3, Google Cloud Storage) or Data Warehouses (Redshift, Snowflake, BigQuery) for structured and processed data. The choice of storage depends on the type of data and its intended use.
4. Data Processing: This is where the transformation happens. Data is processed, cleaned, filtered, and sometimes enriched before it is ready for analytics or reporting. Tools like Apache Spark, Apache Flink, and AWS Glue are often used to handle large-scale data processing.
5. Data Serving: The ingested data is stored in centralized repositories. These may be Data Lakes for raw data (Hadoop, Amazon S3, Google Cloud Storage) or Data Warehouses (Redshift, Snowflake, BigQuery) for structured and processed data. The choice of storage depends on the type of data and its intended use.
6. Data Access and Visualization: Finally, the transformed data is made accessible through business intelligence (BI) platforms like Tableau, Power BI, or Looker. Data engineers ensure that data pipelines are automated, secure, and performant so that stakeholders can make data-driven decisions effectively.

Role of Data Engineer: Understanding the Need of Data Engineers

In today’s data-driven world, Data Engineers play a pivotal role in transforming raw data into valuable insights. With the increasing volume, variety, and velocity of data, businesses require experts to design, build, and maintain systems that can handle, process, and analyze this data effectively. Data Engineers act as the backbone of data-driven organizations by ensuring that data is accessible, clean, and ready for analysis.

Here’s a deeper look into the role of a Data Engineer and why they are essential:

Final Thoughts: Why Big Data Is No Longer Optional for Application Architects.They will need data-driven applications that integrate AI, Big Data, and Real-time Analytics.
As an Application Architect, my job will no longer be about APIs but rather about:

• Data Pipelines
• Real-time Event Processing
• AI Model Integration

This is why I am learning Big Data now — to become a Data-Aware Application Architect.