Since 2006, Apache Hadoop has been a frontrunner in the big data world. Its data collection, storage, and analytical abilities have been instrumental in the rise of the Internet of Things (IoT), which delivers ever-increasing amounts of data from a myriad of sources both inside and outside of the enterprise. Apache Hadoop platforms serve as the basis of next-generation data platforms and data applications, augmenting existing data warehouses for organizations of all sizes, from internet giants such as Facebook and Twitter to Fortune 500 companies such as Kaiser Permanente and Procter & Gamble, to fledgling startups.
The Origins of Apache Hadoop
Apache Hadoop emerged as a solution to roadblocks that littered the young big data environment — namely cost, capacity, and scalability. It utilized an approach that was vastly different from the existing data warehousing strategy. Instead of breaking data down via extract, transfer and load processing and then storing the information in structured silos with relational databases, Apache Hadoop creates “data lakes” that keep the information in its original form. It’s been an open source movement and ecosystem since its inception, making it versatile and, more importantly, cost effective.
Benefits of Apache Hadoop
Apache Hadoop is more than a data storage system. It can be used to efficiently process and analyze data, with benefits reaching across industries.
The extract, transform and load (ETL) method of information processing parses “unstructured” or semi-structured data such as images and PDF files into structured forms identified by markers, such as metadata. While this method can keep the data organized, the problem here is that the rest of the information is often discarded by the system, leaving holes in the files and making it difficult to visualize the data in its complete, original configuration. Apache Hadoop or the data lake approach uses a file system that eliminates the need for ETL processing in advance of loading data, allowing enterprises to keep their data intact and in its original form, yet having the flexibility to structure it as needed on top of the file system by different data applications.
Cost-Effective Data Retention
With data volumes soaring, it often becomes quite expensive to maintain storage. This is one of the reasons ETL processing and aggregated structures can be so popular; they reduce the amount of data in the warehouse to the barebones, thereby decreasing costs. However, there is valuable data that could very well be lost when the “excess” is discarded. The Hadoop distributed file system (HDFS) allows companies to keep all of the raw data it collects in a more cost-effective system, often called a data lake or data hub.
This ability to keep data intact also offers a level of flexibility that’s not possible with most legacy data systems. Users can collect, store, analyze and visualize any type of data from the Internet of Things. This includes documents, images, network logs, emails and social media, and clickstream data.
Apache Hadoop runs on parallel DataNodes. This means that data is distributed among multiple nodes and if one or more nodes fail, the others can seamlessly pick up the slack.
As an enterprise’s data needs increase, Hadoop can be scaled by simply adding more nodes. This ensures that businesses only use the storage space they need while simultaneously being able to quickly increase capacity during usage spikes.
Users can modify the source code as they see fit, making Hadoop accessible and adaptable for any enterprise. The Apache Software Foundation (ASF) enables a community that collaborates and sets standards such as API’s. This open approach also allows organizations to move from one vendor to the next without having to make significant changes to their data storage structures or pay costly conversion fees.
Apache Hadoop Integrations
Apache Hadoop has become a leader in large-scale data frameworks and applications. Its innovative data lake storage strategy and plethora of related tools enable Hadoop systems to conduct a wide range of mission-critical data collection, exploration and analytics tasks on even the largest data sets. Apache Hadoop is able to incorporate a number of applications as sampled below, making it one of the most well-rounded data platform solutions available:
- Apache Spark, often thought of as a rival, can actually be integrated into the Hadoop environment to speed up and enhance data processing with in-memory computing functions.
- StreamSets is a data collector that allows users to create and manage data flows for real-time ingestion of streaming data.
- Trifacta provides “data wrangling,” which is the process of transforming raw data into a format suitable for analysis or other business purposes.
- Waterline Data gives businesses the ability to catalog and automatically classify, tag, and organize data as it’s collected by the system.
There are also real-time visual analytic platforms, such as Arcadia Data, that provide data discovery, visualization, and business intelligence functions.
The Future of Apache Hadoop
Organizations across every sector have found uses for Hadoop-based systems. From dissecting customer behavior and conducting risk assessments to analyzing internal operations and discovering more efficient processes, Hadoop has become a growing influence on big data management across every industry. This impact is likely to expand as the software and its related applications continue to evolve.
One of the most significant changes in the works is a more efficient way to establish data security policies that are uniform across all of the systems within the Hadoop framework. This will enable enterprises to keep their data secure without jumping through numerous confusing hoops whenever data is moved.
Apache Hadoop has also quickly evolved beyond its image as a heavyweight in the web-based enterprises, such as Amazon and Yahoo. It’s quickly becoming an integral part of conventional enterprises that find themselves inundated with petabytes of valuable data. In the future, an increasing number of companies will turn to Apache Hadoop-based systems to do the heavy lifting of storing, extracting and analyzing information.