Learn how an Enterprise Data Hub, powered by Apache Hadoop forms the ideal solution for customers with data stored in various locations. An Operational Data Store (ODS) aggregates data from multiple sources to be combined, cleansed and prepared for downstream operational and analytical use.

 

Introduction

Data has become a competitive advantage for information-driven enterprises that have the ability to effectively operationalize it across their business. This includes discovering and embedding past, present, and future-looking analytics into their end users’ workflow in order to move the metrics that matter. However, if this data doesn’t reach the end consumer in a timely manner, then data is left out of analyses, latency occurs in applications, and end users don’t get the information they need. This, in turn, creates a negative return on data investments.

operational_data_store-hadoop
source: Cloudera

 

Challenges

Countless challenges arise for a wide spectrum of individuals in an enterprise if an effective ODS is not in place.

Limited Data Access – The business is demanding new data sources while encompassing even greater history to fuel daily decisions. Since unstructured data must be reformatted to fit into a relational schema before it can be loaded into the system, it requires an extra data processing step that slows ingestion, creates latency, and eliminates elements of the data that may become important down the road.

Processing Inefficiencies – As data volumes grow and the complexity of data types and sources increases, data processing workloads take longer to run and the time available for reporting and analysis is reduced. In many cases, enterprises struggle to meet SLAs, taking days to process data which leads to “unvaluable” data being archived.

Data Archived and Deleted – As strains are put on traditional systems, and IT try to meet SLAs, “unvaluable” data is archived or even deleted in order to free up capacity for optimal performance. With this historic data made unavailable, it cannot be used in key analytics that can be critical for business decisions.

 

Solution

The implementation of an enterprise data hub (EDH), powered by Apache Hadoop, provides enterprises an ODS that unlocks value by processing and storing any data type at massive volumes—eliminating the need to archive data—while allowing for quick, familiar data access to end users and applications.

Access More Data – Your business users want quick access to new information of all kinds, whether internal or external to the enterprise, while using existing tools. Leveraging an EDH, powered by Apache Hadoop allows enterprises ingest, process ,and store any volume or type of data from multiple sources in full fidelity.

Optimized Data Processing – ETL workloads that previously ran on storage systems can migrate to the EDH where they run in parallel in order to process any volume of data at speed. Optimizing the placement of these workloads frees capacity on traditional systems, allowing them to focus processing power on business-critical OLAP, reporting, and other applications.

Automated Secure Archive – An ODS, powered by Apache Hadop, offers one secure place to store all your data, in any format, any volume, for as long as needed. This allows you to naturally process and store
data without having to ever worry about archiving it, while providing storage for replay when needed. This enables enterprises to deliver historic data on-demand to satisfy internal and external analytic needs data.

Many enterprises that are leveraging an EDH as an ODS have already seen significant returns. These enterprises have been able to free up their existing systems, increase the amount and type of data they collect, and store all of their data in active storage with no need to archive it. This has changed the way they view data processing by providing them the flexibility and scalability that traditional systems have struggled with.