December 16, 2020
No Comments
Guaranteed Results and Success
Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
What is your team’s probability of success?
treat everyone the same.
Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod
What is your team’s probability of success?
Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod
Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
GWEN SHAPIRA
Software Engineer, Confluent. Author of Kafka the Definitive Guide and Hadoop Application Architectures
Michael Zargham, PhD
Big Data Presents Big Payoffs but NOT Before Big Problems Need to Be Solved — preferably BEFORE the project begins. But since that’s not always possible, the sooner expert mentorship can intervene (once issues first appear) — the quicker the project can be put — not just back on track — but rather, back on the fast-track. The data tells the story. Plus, the big reason WHY most initiatives get stuck, stall, struggle, suffocate:
Most of those 15% winning companies received high-level, masterful mentoring, hands-on expert help throughout the process — to hit that “success mark.” Some openly talk about that help and most understandably don’t.
Cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam.
Cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
Cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat.
Massive Datasets
Cons ectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat.
1. Start off by assessing the team and project
2. Use case evaluation
3. Management and technical training
4. Architecture review and evaluation
5. Project planning and creation of crawl, walk, run phases
6. Programming and architecture support while coding
It was frustrating. We were failing and the Vice President was watching us do it. We had all the books and tried to work through the steps, but our use of relationship database systems like MySql and Oracle meant we were approaching Hadoop in a fundamentally incorrect way. Our RDBMS solution was slowing down the Big Data solution.
Within two days of finishing the course we had rewritten major components of the database architecture. Over the next six months we completely overhauled everything we had done before we worked with Jesse. The 14 billion rows of data that used to take 16 hours to run were running in 2 hours, and the upgrades saved our company millions of dollars. If you take into account the lost time, and the salaries of our team while we were failing we could have saved a lot more — we really should have worked with Jesse sooner.
STEVE R.
“The 14 billion rows of data that used to take 16 hours to run were running in 2 hours, and the upgrades saved our company millions of dollars.”
STEVE R.
You will be subscribed to our mailing list. We hate spam as much as you do. You’ll receive Big Data-related emails from us.