Overview
It’s been more than 15 years since Hadoop hit the scene. At one time, it seemed like everyone was talking about the open-source framework with the distributed file system that could provide massive data storage and processing cheaply. Now? Well, not so much.
Hadoop is credited with spawning dozens of startups and driving hundreds of millions of dollars in capital investments. A primary benefit of Hadoop is its ability to inexpensively store structured, semi-structured, and unstructured data. Because the data is stored in a distributed environment across clusters of computers, data is processed in parallel for faster results. Hadoop also makes data easy to retrieve for users.
When “big data” became a buzzword more than a decade ago, Hadoop provided a solution to cost-effectively store and process constantly-evolving data types. This allowed organizations to quickly determine the value of that data and decide if they wanted to perform deeper analysis on it. But as explained in a blog, the drawback is that Hadoop does a poor job of managing the core data for an enterprise.
“When it comes to managing data in a way that is shared across the enterprise, nothing beats a database – and Hadoop is no database. There was no data type safety and no workload management,” according to the blog.
Native Object Store Offers a Modern Approach
Technology innovations can happen fast. Sometimes incredibly fast, which is why the once-popular Hadoop has fallen out of favor as newer solutions became available. One of these technologies is object storage. Object storage offers what’s great about Hadoop—cheap storage capabilities along with support for flexible data types. Yet object storage also goes beyond what Hadoop delivers—storage is three times cheaper and object storage supports data types such as audio, video, and image files that are used by artificial intelligence.
Many organizations are moving away from their legacy Hadoop systems and turning to object storage technologies to lower costs and modernize their big data environments. This evolution in data lakes and data storage is seeing object stores becoming the repository of choice to capture, refine, and explore any form of raw data.
Object storage is essential for analytics because it allows massive volumes of data to be brought together for analysis. The more data that’s analyzed, the more accurate the results.
Teradata offers Native Object Store (NOS). NOS is a capability of the Teradata platform that lets users perform read-only searches and query CVS, JSON, and Parquet format datasets located on external object storage platforms. It allows users to leverage the analytics power of Vantage against data in object stores such as Amazon S3 and Azure Blog Storage. NOS offers a modern, economical approach for companies looking to phase out their Hadoop infrastructure.
Migrate from Hadoop to Gain Performance and Scalability
World-class companies need technologies that can keep pace with growing data volumes while helping to accelerate their digital transformation and meet other business priorities. It’s why innovative organizations are shifting to a connected multi-cloud data platform for enterprise analytics. This type of platform offers the hyperscalability and high performance that Hadoop can’t match.
Organizations still using Hadoop are finding that its overall complexity can restrict their ability to quickly respond to changing business requirements. As a result, many of these companies are looking to migrate to a platform that offers the scalability, performance, and cost effectiveness business users and analysts need, and without the complexity.
When choosing an alternative to Hadoop, companies should consider five factors: these considerations—ease of use, analytical ecosystem integration, flexible deployment options, performance and scalability, and migration expertise—help make the migration as fast and pain-free as possible.
Some companies are migrating to Teradata to gain analytic agility and other benefits. Teradata offers a Hadoop Migration Program to move to a modern platform in three simple steps. The program quickly and seamlessly migrates existing Hadoop data and workloads to Vantage and cloud object storage using proven migration methodologies and tools.
Unify Everything for a Connected Data Analytics Ecosystem
Data must be integrated for a single source of truth. Siloed data becomes quickly outdated and limits insights. That’s why modern enterprises need the ability to integrate all of their data, including data in Hadoop-based data lakes, by using a connected multi-cloud data platform. Teradata unifies everything, including data lakes, data warehouses, analytics, and new data sources, which delivers unlimited intelligence to the business.
While Hadoop provided a problem-solving solution in the early days of big data, it’s no longer cutting edge. Today’s companies need a platform with the flexibility to handle current and future massive and mixed data workloads. This includes the ability to create a seamless experience for ingestion, exploration, development, and operationalization. Connecting and analyzing all data at scale gives companies a complete view of their business across data lakes, object stores, cloud services, and any other part of the ecosystem for maximum insights.