A Deep Dive into Hadoop Ecosystem

As we have already seen with the Big Data and Hadoop components (Article 1 , Article 2), Hadoop Ecosystem is always changing and being adapted for new applications. Large-scale datasets can now be stored, processed, and subjected to data-oriented analysis thanks to the Hadoop ecosystem, which has grown to be an essential component of the big data technology stack. Even while Hadoop is still useful for some tasks, modern data stack frequently combine it with other technologies to manage the volume and complexity of data.

Apache Hive Essential (2nd Edition) | Source: https://www.packtpub.com/

Overview of Hadoop Ecosystem

Various number of tools and technologies have emerged that increase Hadoop's functionality and broaden its applicability. These technologies offer a variety of large data processing functionalities, including analytics, data processing, data management, and data storage.

Apache HBase

Apache HBase is an open-source, distributed, and scalable NoSQL database that excels in handling large volumes and built on HDFS (Hadoop Distributed File System) and MapReduce. HBase basically a column-oriented data storage that stores data in a sparse, where many columns may be null for a given row. As a core component of the Hadoop ecosystem, HBase seamlessly integrates with other Hadoop technologies like HDFS, MapReduce, Hive, and Spark.

Key feature of HBase:

• Strictly consistent reads and writes

  Apache HBase is a great option for real-time messaging or analytics applications because of its quick read and write speeds. HBase uses a Log-Structured Merge-tree (LSM-tree) data storage architecture, which reduces disk seeks by merging small files into larger ones.

• Automatic failover support

  Apache HBase can automatic failover when a node fails to ensure high availability and continuous operation in the event of node failures. Make it suitable for high availability online transaction processing (OLTP) applications.

• Automatic data sharding

  Apache HBase provides consistent performance for read and write operations. Tables are automatically sharded (divided) into regions and distributed across RegionServers, optimizing data access and management.

How To Install Apache HBase | Source: https://thecustomizewindows.com/

Apache ZooKeeper

Apache ZooKeeper is an open-source server for highly reliable distributed for maintain configuration data, synchronization service, and naming registry for large cluster. ZooKeeper offers the name space known as znodes in a distributed system to guarantee that several nodes that must coordinate their operations and interact with one another nodes. ZooKeeper is widely used in distributed systems such as Hadoop, Kafka, and HBase, and other distributed application.

Key feature of ZooKeeper:

• High Availability and Fault Tolerance

  ZooKeeper is built to be highly available and fault-tolerant. In order to maintain service functionality even in the event of server failure, it uses a group of servers (usually an odd number) to form a quorum.

• Sequential Consistency

  ZooKeeper uses a hierarchical namespace, which is similar to a filesystem, to store and manage configuration data for convenient access and updates, ensuring a consistent system state across all nodes. It also maintains sequential consistency, ensuring that client updates are applied in the same order as they were sent.

• Distributed Coordination

  ZooKeeper provides primitives such distributed locks, barriers, and queues to coordinate processes effectively. This is important for managing the complex interactions between various components like Hadoop HDFS, YARN, HBase, and Kafka.

Setup Zookeeper Cluster | Source: https://dataview.in/

Apache Pig

Apache Pig is a high-level programming language specifically made for processing big data collections and built on top of Hadoop. Originally developed by Yahoo Research around 2006 in order to simplify the process of creating unprocessed Java MapReduce programs. Apache Pig reduces the amount of time needed to write MapReduce applications, making it ideal for ETL (Extract, Transform, Load) activities.

Use case of Pig:

• Data analysis and reporting

Pig makes it accessible to both developers and analysts by fusing procedural capabilities with a syntax similar to SQL. Yahoo scientists scan through petabytes of data using grid techniques to find important insights and produce reports.

• ETL and data warehousing

Pig is a tool for creating data warehouses and extracting, transforming, and loading (ETL) large amounts of data. Pig query language made building data pipelines rapid and simple.

• Data integration

Pig allows data to be integrated from multiple sources, such as HDFS, HBase, and relational databases, into a unified processing pipeline. Pig work under open-source project Hadoop, it can transform and optimize the data operations into MapReduce.

Mastering Hadoop 3 | Source: https://www.oreilly.com/

Apache Sqoop

Apache Sqoop is an application uses a command-line interface to move data between Hadoop Distributed File System (HDFS) and relational databases. In addition to using MapReduce for effective, parallel, and fault-tolerant data operations, it provides data transformation capabilities. Internally by using Sqoop commands, it can translated into MapReduce jobs that run on Hadoop Distributed File System (HDFS).

Use case of Sqoop:

• Import/Export from Hadoop

  Data that has been processed or analyzed in Hadoop can be imported or exported into relational databases using Sqoop. Sqoop imports and exports data using the YARN architecture.

• Incremental Data Loading

  Sqoop have capability for incremental imports, allowing for efficiently transferred newly added or modified data from RDBMS to Hadoop. It supports both full and incremental load and can load the entire table or specific sections of the table with a single command.

• Kerberos Security

  Kerberos provides mutual authentication between the Sqoop client, Sqoop server, and other Hadoop components like HDFS and YARN. The Kerberos computer network authentication protocol, which Sqoop supports, allows nodes to communicate over an unprotected network in order to securely authenticate users.

Steering number of mapper (MapReduce) in Sqoop | Source: https://dataview.in/

Apache Mahout

Mahout is open-source framework used to develop scalable machine learning algorithms and data mining libraries for tasks like clustering, classification, and recommendation. These machine learning algorithms are implemented on top of Apache Hadoop using the Hadoop MapReduce, Spark, H2O, and Flink. Depending on the complexity of the jobs, Mahout can handle enormous volumes of data more effectively by processing it in a distributed setting.

Key feature of Mahout:

• Scalability

  Mahout is built to manage extremely large datasets and facilitates the ability to scale horizontally through the use of distributed processing architectures.

• Rich algorithm library

  Mahout utilizes parallel processing to enhance the speed of variety of machine learning algorithms such as collaborative filtering, customer segmentation, and fraud detection.

• Flexibility and Extensibility

  The flexible construction of Mahout enables easy integration with MapReduce for batch processing, and Spark for real-time processing, and Flink for scalable and cost-effective machine learning workflows.

Apache Mahout Essentials | Source: https://www.packtpub.com/

Apache Flume

Apache Flume is a distributed, dependable, and accessible system for effectively collected, aggregated, and moved from numerous sources to a centralized large volumes of log data. Although Apache Flume is unable to process the data., it can gather enormous data logs from a source and store them while keeping track of every event. Flume can transports large amounts of streaming data to the Hadoop Distributed File System (HDFS) for additional processing and to various storage solutions like HBase or Solr.

Key feature of Flume:

• Scalability

  Flume is crafted to manage handle high-volume data flows and supports horizontal scalability, allowing for the addition of more agents to increase its capacity. The agent is a JVM process in Flume. Persistent Java Virtual Machine(JVM) process that hosts the core components, agents responsible for transporting event (data) from an external source to a designated destination.

• Fault-tolerance

  Flume supports reliable and durable message delivery, guarantees that no data is lost during the ingestion process. Flume design also accommodates recovery from various failures, including network issues, hardware issues, or malfunctions with the destination system.

• High throughput, low latency

  Flume is known for its high throughput. However, its effectiveness in low-latency situations, particularly those necessitating sub-second or millisecond responsiveness, however, flume effectiveness in low-latency situations, particularly those necessitating sub-second or millisecond responsiveness, is contingent upon the specific application and configuration. It’s suited for scenarios that require rapid data processing, as it can capture and process data in real time.

Introduction to Flume | Source: https://juheck.gitbooks.io/

Apache Hive

Apache Hive is an open-source data warehousing tool built on top of the Hadoop ecosystem, designed to reads, writes, and processes large dataset easy and efficient. Just like SQL, Hive also support command like User-Defined Functions (UDF), Data Definition Language (DDL), and Data Manipulation Language (DML). Hive uses a transaction manager that built on top of the Hive Metastore to give ACID guarantees with snapshot Isolation.

HiveQL is a query language used by Apache Hive for its operations. HiveQL was created to simplify Hadoop work without needing to write complex MapReduce programs. HiveQL supports various SQL features, such as aggregations, sub-queries, and joins.

Key feature of Hive:

• Schema Flexibility (Schema-on-Read)

  Hive enables older data to be accessed with updated schemas by applying the current schema while the query is running. This indicates that Hive supports schema evolution, which is the process of changing a table's schema while maintaining with current data and queries. The data can be stored in various formats offering flexibility in data storage (such as text, ORC, and Parquet).

• Partitioning and Bucketing

  Hive supports data partitioning and bucketing, which are techniques to organize and optimize data storage and retrieval especially for large tables. Based on the values of one or more partition keys, partitioning separates the data in a table into logical subgroups (e.g. year/month/datetime). Bucketing divides data into a fixed number of buckets based on a hash function applied to a bucketed column.

• Live Long And Process (LLAP)

  Apache Hive supports ACID transactions with Hive LLAP. Hive LLAP sits on top of the query engine to accelerate query and data processing. Hive LLAP can be set up to run extremely small processes to handle basic queries or to scale out and down dynamically as needed.

Apache Hive Methodology | Source: https://www.researchgate.net/

Apache Oozie

Apache Oozie is a workflow scheduling system that operates on a server and is available as open-source software, created to oversee Hadoop tasks like MapReduce, Hive, and Pig. Oozie is also capable of scheduling particular tasks for a system, such as Java applications or shell scripts.

Oozie tool manages numerous Hadoop processes and streamlines the cluster administrators workload by handling numerous component-based responsibilities. Oozie retired in February 2025 and the move to the Attic was completed in April 2025.

Zookeeper and Oozie: Hadoop Workflow | https://www.projectpro.io/

Apache Kafka

Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Apache Kafka was initially developed at LinkedIn in early 2011 to address their need for a high-throughput, low-latency system to handle real-time data streams. Fundamentally, Kafka is a distributed messaging system to communicate with each other by producing and consuming messages (data) in real time.

Using Kafka and YARN for Stream Analytics on Hadoop | Source: https://dataconomy.com/

Alternative Modern Cloud Platforms for Apache Hadoop

Apache Hadoop is still a core technology in modern big data, providing a strong framework for distributed storage and processing of large datasets. However, many Hadoop users face complexity, unscalable infrastructure, high maintenance costs, and total unfulfilled value. Numerous other technologies have emerged to offer commercially-supported alternatives for big data. For some organizations, they often consider the following scenarios:

• Cloud Platform

1. AWS Redshift

Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse service offered by Amazon Web Services (AWS), designed for big data analytics. It enables you to utilize common SQL queries to effectively examine vast volumes of data inside a data warehouse. Redshift supports open data formats such as Apache Parquet or Optimized Row Columnar (ORC) which enables users to quickly complete complex analytical queries.

2. Google BigQuery

BigQuery is a fully managed, serverless data warehouse that enables businesses to store, analyze, and generate insights from large volumes of data quickly and cost-effectively on the Google Cloud Platform. It allows you to utilize regular SQL queries to evaluate big datasets within a data warehouse in a couple of seconds. Organizations may do advanced data modeling and predictive analytics right within BigQuery thanks to its built-in machine learning capabilities.

3. Azure Data Lake Storage

Azure ADLS is a scalable and cost-effective solution for storing both structured and unstructured data, optimized for big data analytics workloads. Data Lake Storage was created from the ground up to support hundreds of gigabits of throughput while handling many petabytes of data. ADLS's vast scalability enables the storage and analysis of petabytes of data.

Azure data lake storage comes in two varieties: ADLS Gen1 and ADLS Gen2. ADLS Gen1 can be accessed from Hadoop using the WebHDFS-compatible REST APIs. ADLS Gen2 is designed for big data analytics which means there is something called azure blob file system or ABFS which is encrypted.

• Specialized Processing Platforms

1. Apache Spark

Apache Spark is an open source analytics engine used for big data workloads that that supports both batch and real-time analytics. The fact that Hadoop MapReduce relies on disk-based processing, which writes intermediate results to disk at the end of each phase, is one of its primary drawbacks. Spark, on the other hand, makes use of in-memory processing, which enables data to be stored in RAM while computation is on.

Key Difference Between Hadoop(MapReduce) and Spark | Image create by author

2. Apache Flink

Apache Flink is an open-source stream processing framework that enables scalable, high-throughput, and low-latency data processing. Spark provides a compromise between micro-batch processing and stream capabilities. Leading the way in stream data processing, Flink guarantees real-time responsiveness for datasets that are always changing.

Key Difference Between Spark and Flink | Image create by author

3. Trino

Apache Trino (formerly PrestoSQL) is a highly parallel and distributed query engine designed for effective, low-latency analytics. With its distributed query execution capabilities, Trino offers a data analytics solution that can be used with data sources of any size, from gigabytes to petabytes. Without requiring data transportation, the Trino design enables it to query data including heterogeneous sources.

Summary

At this point, you might be asking yourself, So, should I choose Hadoop or other tool like Spark? The answer, as is depend on the size or volumes of data and other factor like speed, whether for batch, machine learning, or streaming analytics. While Hadoop remains relevant, modern architectures now pair it with other technologies to better manage complexity and scale.

Tools like Apache Pig, Sqoop, Mahout, Flume, and Hive enhance Hadoop's capability by supporting ETL operations, machine learning, log data ingestion, and SQL-like querying. These tools operate together to provide a complete framework for managing complex data processing jobs in dispersed locations. I hope you enjoyed reading this.