New data sources like social media sites, website logs, mobile devices and sensors generate unprecedented amount of unstructured and semi-structured data. The explosion of new data sources has not only provided organizations new opportunities to grow revenues and reduce costs but has also opened the door to possibilities. But manual processes for reconciling fragmented, duplicate, inconsistent, inaccurate, and incomplete data, as well as fragmented point solutions, result in dubious data and delayed business insights that can’t be trusted.
A systematic approach that quickly and repeatedly transforms ever-increasing amounts of big data into business value without risk is clearly the ingredient for success. MAGNOOS leverages the following solution offerings to make your big data projects successful
The gold standard in data management solutions for integrating, governing, and securing big data that your business needs to extract business value quickly.
The Hadoop eco-system is rapidly changing with new innovations continuously emerging in the open source community. Big Data Management builds on top of the open-source Hadoop framework and preserves all the transformation logic in your data pipelines. As a result, Hadoop innovations are implemented faster with less impact and risk to production systems.
Lower Big Data Project Costs
Expand Hadoop Adoption Across the Enterprise
Minimize Risk of Adopting New Technologies
HParser provides organizations with the solution they require to extract the value of complex, unstructured data. This powerful data parsing capability in Hadoop empowers organizations to achieve new levels of productivity, efficiency and scalability. Organizations can readily augment their existing IT investments by using HParser as the standard for data parsing in Hadoop. Using HParser, customers benefit from an engine-based solution that covers the broadest range of data formats and greatly simplifies and speeds the analytical process by eliminating the risks and costs of one-off custom-coded parsing scripts.
Ensure the success of big data analytics projects by uncovering accurate relationships among connected data.
Improve big data analytics – Since big data systems integrate data across multiple internal and external systems, the data can be inconsistent and duplicated. Big Data Relationship Management matches duplicate party information within and across multiple sources and links it to create the most accurate information
Infer non-obvious relationships – Big Data Relationship Management infers non-obvious relationships among parties to automatically discover people within a household, organization, or a locale. It sorts parties based on common attributes, and groups them to create a 360-degree view of the party. The result: You can search and view the relationships in real time
View social relationships – With Big Data Relationship Management, you can discover and visualize relationships across vast amounts of disparate data brought in from social media. It creates and actively maintains the relationships by appending any new information, internal or external, about the party, such as social, demographic, and interaction data from sources like Facebook and LinkedIn
Receive results rapidly – You get rapid results since Big Data Relationship Management processes billions of records of data within hours. Since social media produces vast amounts of data, now business users see accurate and related information about parties in real-time — and can perform their daily tasks more efficiently
The sheer volume of data being ingested into Hadoop systems is overwhelming IT. Business analysts eagerly await quality data from Hadoop. Meanwhile, IT is burdened with manual, time intensive processes to curate raw data into fit-for-purpose data assets. Big data cannot deliver on its promise if it brings progress to a grinding halt because of complex technologies and additional resources required to extract value.
Intelligent Data Lake enables raw big data to be systematically transformed into fit-for-purpose data sets for a variety of data consumers. With such an implementation, organizations can quickly and repeatedly turn big data into trusted information assets that deliver sustainable business value.
Enterprise Data Catalog enables Business and IT users realize the full potential of their enterprise data assets by providing a unified metadata view that includes technical metadata, business context, user annotations, relationships, data quality and usage. Discover, classify, and govern your data with visibility into the end-to-end lineage of all data assets cross the enterprise.
Intelligent Streaming allows organizations to prepare and process streams of data and uncover insights while acting in time to suit business needs. Intelligent Streaming provides pre-built high-performance connectors such as Kafka, HDFS, NoSQL databases, and enterprise messaging systems and data transformations to enable a code-free method of defining your data integration logic. And data flows can be scheduled to run at any latency (real time or batch) based on the resources available and business SLAs.
Derive maximum value from IoT streams by gathering and analyzing the information immediately and at an ever increasing scale
Enable real-time operational intelligence with big data streaming analytics
Reduce time-to-value with increased productivity and rapid deployment
Deliver information at any latency with one flexible platform
Simplify configuration, deployment, administration, and monitoring of real-time streaming
Minimize risks associated with complex and evolving open source technologies
Designing a Big Data solution is a complex task, considering the volume, variety and velocity of data today. Add to that the speed of technology innovations and competitive products in the market.
Proliferation of tools in the market has led to a lot of confusion around what to use and when, there are multiple technologies offering similar features and claiming to be better than the others.
MAGNOOS can help you analyzing your business problem objectively and identify whether it is a Big Data problem or not? Once that decision is made there are number of scenarios that needs to be considered while designing the Big data solution like Form and frequency of data, Type of data, Type of processing and analytics required.
MAGNOOS can help you to find the right technology as per your requirement and add more value with our own experience. We can help you step by step to build big data solution that suits your organization use cases.