With the Google Cloud Platform, companies can quickly and cost-effectively keep up with the pace of today’s digital transformation. The Google Cloud Platform allows businesses to build and run applications, and store and analyze data, all on Google’s Cloud. Google has made major investments to meet the demands of the enterprise market and have
There are new technologies popping up daily in the world of Big Data. It’s impossible to keep up with them all and it’s even more challenging to predict the winners and losers. However, an influential player in 2015 was Apache Spark, which is fast and general engine for large-scale data processing. Interest in this open
Hadoop is all the rage right now. In my 20 years in IT, I have never seen so many people enamoured with something that most of them don’t understand. I equate it to a situation where there is a line full of people waiting for a free give-away. Countless others will join the line without
Now that we’ve discussed the full lifecycle for implementing data analytics capabilities and deriving insight, how does one sustain it going forward? In the past we participated in data warehousing or data analytics projects that were seemingly successful and received positive reviews when completed. However, when we touched base with the team a year later
The Revolution At the end of 2007 classic, Competing on Analytics, Tom Davenport predicted the rise of “analytical amateurs,” where the enterprise extends its frontline data analytics capabilities across the organization in its effort to become a data-driven enterprise. That transformation would be done largely without assistance from traditional information technology department capabilities and processes.
Last week, we introduced the assessment criteria for selecting the right tools for your big data platform. This week in the Big Data Diamonds series, we fill focus on using Agile implementation and methodology to continuously drive value and insight. Maven Wave employs the Agile methodology for delivering solutions to our clients and the incremental and iterative
Now that you have identified the components required to define your big data architecture (as discussed in last week's Big Data Diamonds blog post), we will introduce the assessment criteria for selecting the right big data tools. Selecting the right tools for your big data platform is not as easy as selecting the best application on
In last week's Big Data Diamonds blog post, we discussed the importance of assessing your data to ensure that you understand it. This week we are introducing the components required to define the big data architecture. Once you understand the data and business objectives, you are equipped with the inputs necessary to define a robust architecture.
In our last Big Data Diamonds blog post, we discussed the importance of building a team with the right skill sets. Now that you have the right big data team in place, it's important to assess your data to ensure you really understand it. A fundamental mistake many companies make is to define a big
Most great accomplishments are not delivered by great ideas alone. It takes a team with the right skills to deliver a quality result. The same holds true when delivering a big data project. In our last Data Diamonds blog post, we talked about starting any big data project with a well-defined purpose and the importance of