2018 Dataworks Summit is just around the corner. As you’re preparing your travel to San Jose, it’s time to think about how to maximize your time at the Dataworks Summit. Dataworks Summit will take place from June 18 to June 21. Sessions, keynotes, and workshop are spread across eight different tracks. Check out the full agenda. Everyone may have different goals for this summit. While you’re going through the agenda to select the best sessions for you and your organization, here are our recommendations.
It may seem like only yesterday that we said goodbye to 2017, but we are almost half-way through 2018. Big things in Big Data happened in the month of April. Many of us watched Mark Zuckerberg testify in front of Congress about data use and security, and still await the final outcome of Cambridge Analytica’s data abuse. If April seemed like it slipped under your fingers, check out what you might have missed in the world of big data.
March is gone and Spring has arrived, at least for many of us. A lot happened in March, and we certainly don't want you to miss out on what’s big on big data. Without further ado, here is what you might have missed in March.
Poor February. The short month is dismissed for its brevity (let’s not talk about the weather) but a lot transpired the past 28 days, especially in big data and analytics. ICYMI, here’s a recap of the top stories:
It seems like only yesterday that we all gathered for the Strata New York Conference. And yet here we are, March is around the corner, and Strata San Jose is just a month away. Historically, Strata San Jose has roughly 5000 attendees while Strata New York averages closer to 7000 attendees. As one of the largest Hadoop conference in the US, Strata sessions focus on using data for competitive advantage. Strata Conference is also an opportunity to hear real life stories from enterprises who have been there, have the scars, and wrote the book. Strata is the ideal place to understand trends in the big data world. If you missed it, here are the [trends from 2017].
To learn from successful from Cloudera customers on how they succeed in BI on Big Data, check out this best practices webinar
The annual Gartner Data & Analytics Summit is just around the corner. As in past years, we are all anticipating the overwhelming sessions and agenda throughout this exciting week. This time in Grapevine, Texas (again)! In between sessions, catch a breath of fresh air and check out the exhibit hall to collect a bag of goodies to bring home. While T-shirts always make good pajamas, we may also wonder which sessions and vendors we should not miss. With all of the sessions available at the Summit, here are our suggestion on the ones you don’t want to miss!
Rumor has it that with the rise of Apache Spark, Spark will replace Hadoop.
Well, let’s take a look. Apache Spark is an open-source processing engine that supports interactive quieries while Hadoop is an easy to scale, cost effective data storage. The truth is- Spark does not replace Hadoop, in fact, Hadoop and Spark complement one another.
Now you may wonder: how will Spark and Hadoop affect your big data strategy?
In its early days, Hadoop was chosen because it was much cheaper to store large amount of data, compared to Enterprise Data Warehouse . However, Hadoop required users to have strong technical background to be able to query or do anything on Hadoop. Therefore, the assumption was that Hadoop was only good for data storage.
Today, Hadoop is still the best option for inexpensive data storage. And the reality is as more technologies developed, Hadoop has become more and more user friendly too. In fact, the latest Big Data Maturity surveys indicate that in addition to the traditional data storage warehouse capability, a significant number of companies are using Hadoop for BI.
Companies like Yellow Pages are seeing sub-second BI query response time on Hadoop and have been able to drive increased Hadoop adoption across their organization. If driving Hadoop adoption has been a concern for you in the past, maybe you should reevaluate Hadoop for BI now.
Since the 1980s, the world has been using OLAP technology to provide a business interface to analyze data stored in traditional ERP and CRM systems. As the demand for insights increased, MOLAP and ROLAP became key technologies.
With all of the different OLAP options out there, you may wonder which one can actually help you achieve your big data strategy. Which strategy is most suitable for your Hadoop environment?
Research shows that the average enterprise has at least 6 to 10 Business Intelligence tools. Microsoft Excel, the world's most prevalent analysis tool is used by 1 Billion users.
Other companies like Tableau, Qliktech, MicroStrategy or Business Objects have had great success too.
However, there is one key issue with a heteregeous BI environment: each tool uses a different protocol so data has to be customized to work with each BI tool.
For instance, Excel uses MDX and Tableau prefers SQL. What if your company uses new tools like Looker or even open-source tools like Apache Zeppelin?
How can your deliver one version of the truth to all users, regardless the tool they use? This post helps you get there.