If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all.
The IBM Integrated Analytics System (IIAS), is a unique, cloud-ready appliance and machine learning platform wields the power of an in-memory, massively parallel processing database engine with embedded Spark. It also runs on market-leading IBM Big Data Servers and IBM FlashSystem 900 storage
So what happens now when we go beyond the frontiers of the data warehouse and into the world of the data lake? – the world of Hadoop, of NoSQL, the world of schema on read, of discovering the data as is? For many organizations, the holy grail is to reap the benefits of the data lake while retaining
The greatest grandmasters in chess think five moves ahead. In IT, even thinking five moves ahead isn’t enough. A lot of things can happen, planned and unplanned, within the first five moves of an IT strategy deployment that cause a significant amount of disruption both concurrently and long
Managing enterprise information has always been a good idea, however with the potential for looming penalties from the General Data Protection Regulation (GDPR) non-compliance, companies are waking up and some organizations are even seeing GDPR as an opportunity to establish strengthened
How did companies like Facebook and Airbnb get so big so fast? What can we learn from them? Why is data so important for growth? Nancy Hensley, Director of Strategy & Growth for IBM Hybrid Cloud, has the answers in this episode of Making Data Simple.
Although there are many new and emerging classes of data integration, quality and governance software tools available in the market, many large organizations are coming to the conclusion that they're best served by a single unified enterprise data integration, quality and governance platform that
Learn how the IBM Integrated Analytics System, a unified data platform built on the IBM Common SQL Engine, helps do data science faster with high performance, embedded machine learning capabilities and built-in tools for data scientists to deliver analytics critical to increasing your organization’
It’s no secret that most successful businesses today depend on data—and that’s more true of technology companies and start-ups than any other segment. The ability to analyze information, derive insight quickly, and make confident, timely decisions can often help a software company break into a new
Upon reading his own obituary in the newspaper, famed author Mark Twain is said to have remarked that reports of his death were greatly exaggerated. I can only imagine that if the data warehouse appliance were a 19th century American novelist, it might say the same thing. For a while now,
Data, insights, cloud, agile, analytics. These are all terms that get thrown around a lot in technology these days. But the truth is that unless you can combine some or all of these concepts, the bottom line benefit to your business will likely not as great as you may expect.
It’s easy to be blinded (and impressed) with the rapid innovation and evolution in the arena of big data. Today’s most technically sophisticated companies have the opportunity to exploit big data tools to address mind-numbingly cool use cases and produce very enticing results. However, so many
Data models for developing data warehouses need to evolve for managing and defining data lakes. This first installment of a blog series on charting the data lake introduces the potential role of data models in data lake environments and how they need to take an active role in defining and managing