For growth initiatives, many companies are looking to innovate by ramping analytical, mobile, social, big data, and cloud initiatives. For example, GE is one growth-oriented company and just announced heavy investment in the Industrial Internet with GoPivotal. One area of concern to many well-established businesses is what to do with their mainframe powered applications. Mainframes are expensive to run, but the applications that run off of them are typically very important and the business can not afford to risk downtime or any degradation in service. So, until now the idea of modernizing a mainframe application has often faced major roadblocks.
There are ways to preserve the mainframe and improve application performance, reliability and even usability. As one of the world’s largest banks sees, big, fast data grids can provide an incremental approach to mainframe modernization and reduce risk, lower operational costs, increase data processing performance, and provide innovative analytics capabilities for the business—all based on the same types of cloud computing technologies that power internet powerhouses and financial trading markets. Continue reading →
Just like we saw in the dot-com boom of the 90s and the web 2.0 boom of the 2000s, the big data trend will also lead companies to make some really bad assumptions and decisions.
Hadoop is certainly one major area of investment for companies to use to solve big data needs. Companies like Facebook that have famously dealt well with large data volumes have publicly touted their successes with Hadoop, so its natural that companies approaching big data first look to the successes of others. A really smart MIT computer science grad once told me, “when all you have is a hammer, everything looks like a nail.” This functional fixedness is the cognitive bias to avoid with the hype surrounding Hadoop. Hadoop is a multi-dimensional solution that can be deployed and used in different way. Let’s look at some of the most common pre-concieved notions about Hadoop and big data that companies should know before committing to a Hadoop project: Continue reading →
The world’s largest banks have historically relied on mainframes to manage all their transactions and the related cash and profit. In mainframe terms, hundreds of thousands of MIPS are used to keep the mainframe running these transactions, and the cost per MIP can make mainframes extremely expensive to operate. For example, Sears was seeing the overall cost per MIP at $3000-$7000 per year and didn’t see that as a cost-effective way to compete with Amazon. While the price of MIPS has continued to improve, mainframes can also face pure capacity issues.
In today’s world of financial regulations, risk, and compliance, the entire history of all transactions must be captured, stored, and available to report on or search both immediately and over time. This way, banks can meet audit requirements and allow for scenarios like a customer service call that results in an agent search for the transaction history leading up to a customer’s current account balance. The volume of information created across checking, savings, credit card, insurance, and other financial products is tremendous—it’s large enough to bring a mainframe to its knees. Continue reading →
The new database is opening up significant career opportunities for data modelers, admins, architects, and data scientists. In parallel, it’s transforming how businesses use data. It’s also making the traditional RDBMS look like a T-REX.
Our web-centric, social media, and internet-of-things are acting as a sea-change to break traditional data design and management approaches. Data is coming in at increasing speeds, and 80% of it cannot be easily organized into the neat little rows and columns associated with the traditional RDBMS.