Watch this video to learn why providing ad hoc data access for business users is a great idea -- in theory. But providing it on a budget of limited resources is a balancing act that can be hard to pull off.
While adopting horizontally scalable data storage with a BI front end gives IT the infrastructure to handle increasing user loads, it doesn’t necessarily solve other issues.
For example, in high-variety, big data environments, users need a self-service way to explore data without knowing the precise query they want to answer. But when many concurrent users explore data in this way, it can degrade response time.
In this video, you’ll learn that the root cause of data analytics performance issues can often be traced to differences between traditional business intelligence/data warehousing (BIDW) environments and today’s distributed computing environments.
In terms of data modeling, what worked for BIDW won’t fly in the distributed compute world. For one thing, network throughput wasn’t much of an issue with BIDW. But, it’s very important when compute resources and data stores reside on different machines strung together via a network.
In a distributed scenario, it makes sense to denormalize data because every join operation generates heavy network traffic, and it also pays to focus on query optimization.
It’s great to be able to work with large scale data in distributed systems. But this video explains why the more data you have, the more important it is to optimize data stores for queries at scale. Appropriate partitioning schemes can help although every scheme has inherent limitations.
Likewise, data layout can also substantially reduce seek times for your analytic systems. Hadoop and SQL-on-Hadoop will accommodate a variety of layout options beyond traditional row oriented. Columnar formats like Parquet are more suitable for denormalized data than the row-oriented formats used in relational databases. Parquet functions effectively regardless of the data processing framework, data model, or programming language.
Watch this video to learn why the more aggressively organizations pursue data-driven business strategies, the more individuals need access to data. And, hand-in-hand with access goes security. In a large organization with lots of data and lots of data sources, controlling who has access to what presents quite a challenge.
Allowing access to be controlled at the data source can cause problems with caching and memory management that degrade performance, especially in systems with many concurrent users. Bringing access authorization up to the BI layer can minimize performance issues while avoiding inadvertent security failures caused by users mixing data from multiple sources.
Watch and listen as a panel of experts including Cloudera co-founder and CTO Amr Awadallah and two joint Zoomdata and Cloudera customers, Bidtellect and Markerstudy, explain the opportunities and challenges of using big data to increase customer engagement.
Information managers have always faced challenges. And that hasn't changed in the era of Big Data. In this podcast, you'll hear how today's information managers can use decades of accumulated knowledge to:
Solve the data management challenges of Big Data
Serve IT and the data consumer
Leverage distributed information lifecycle management
Create an agile data stack on-premises, in the cloud, or hybrid
Visualize 1+ billion rows in seconds! Watch the fastest visual analytics in action.
The public sector is even worse. But Blue Canopy bucks conventional wisdom by:
Focusing on the user experience
Delivering a cloud-based analytic ecosystem in 30-60 days
Deliver Important New Customer Insights In Real Time. Join us for a webinar and solution demo to learn how Markerstudy used Zoomdata’s fast visual analytics and Cloudera’s advanced Hadoop technologies to increase revenue and reduce risk!
UK insurance company Markerstudy uses Hadoop, Spark and Zoomdata to speed up analysis and glean new insights from its insurance data.
Empowering Business Users with Big Data, A Practical Approach.
Etleap and Zoomdata have optimized the way analysts extract, transform, load, and analyze data using Amazon Redshift data warehouses. Etleap’s cloud-based ETL tool guides even the inexperienced user through combining virtually any data source into Redshift. And Zoomdata connects directly to data warehouses of any size to visually interact with data in ways that were previously not possible.
Understand total data reporting and its evolution
Zoomdata enables visual big data analytics at the speed-of-thought. Try its unmatched speed on 1 billion rows of data. Users love it, IT finally sees its big data investment shine.
Enterprises need new methods for ingesting, analyzing and acting on data quickly and efficiently. To detect threats, they require tools that enable rapid-fire discovery of data as it’s streaming, throughout their organization and beyond. This IM Live webcast explores the power of micro-queries for diving into big data sets of all shapes and sizes.