Map once, deploy everywhere
Use CoSort or Hadoop: Same job, metadata, GUI
Whether Hadoop or not – the choice is easier than you think.
To manipulate large data volumes, most people think they need a new IT structure like Hadoop or Teradata, an in-memory or columnar database like SAP HANA or Vertica, a DB or ELT appliance like Exadata or Netezza, or a complex ETL tool like Informatica or Ab Initio. Do you have the time, money, and expertise for that?
What if there was a simpler, more cost-effective platform for rapidly processing and managing large datasets, interchangeably leveraging existing file systems, HDFS data, and engines? There is one!
Whether your data sources are in a standard Unix, Linux, or Windows file system, in HDFS, or in the proprietary systems mentioned above, you can access this data in the IRI Voracity platform either with the proven IRI CoSort engine or Hadoop engines interchangeably. Without programming or making any changes, your jobs share the same simple, accessible metadata layer and a free Eclipse IDE for management with graphical job design and execution modes, the IRI Workbench.
How do you plan to multiply large data workloads?
Click to see the seamless processing options that only IRI Voracity offers for transforming, masking, and generating large data sets:
With Hadoop
Without Hadoop
Voracity is no longer just about homogeneous data processed heterogeneously, or vice versa. It's about a seamless, unified, metadata-driven enterprise information architecture that gives you control over disparate data sources and processing engines... and one that meets evolving data integration, governance, and analytics needs.
What is Hadoop?
Hadoop is an increasingly popular distributed computing environment that allows companies to analyze and store large amounts of data.
A big data crisis
Massive amounts of data are growing exponentially, and simply throwing hardware at it is not a complete or reliable long-term solution. IRI's time-tested strategies and software, however, are.
You should use Hadoop when you need to process and store massive amounts of data that wouldn't fit on a single machine. It's particularly useful for: * **Big Data Analytics:** Analyzing large datasets for insights, trends, and patterns. * **Data Warehousing:** Storing and managing large volumes of structured and unstructured data. * **Batch Processing:** Performing complex computations on large datasets that can be processed in batches. * **Real-time Data Processing (with related tools):** While Hadoop is primarily for batch processing, it can be integrated with tools like Spark Streaming or Storm for near real-time analytics on large data streams. * **Cost-Effective Storage:** Storing large datasets on commodity hardware, which is generally cheaper than specialized storage solutions. * **Fault Tolerance:** Its distributed nature ensures that data is replicated across multiple nodes, making it resilient to hardware failures.
Hadoop is not a monolithic framework. You need to know when and how to use it. Voracity makes Hadoop job design and deployment a breeze when you need it.
See also
