Accelerate Your

Helping you use your existing resources more efficiently and building the right team. We guarantee results and success.

Guaranteed Results and Success

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What is your team’s probability of success?

Do you know if your team has the right skills to be successful with Big Data?

Making sure the team has all of the right skills is crucial to your success with Big Data. A team missing critical skills or that has an ability gap have extremely low probablitities of success.

Most Big Data consultancies

treat everyone the same.

Whether they are advanced or just beginners – leaving your team to sort out the mess.

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What is the difference between data science and data engineering?

Does a data scientist or data warehouse engineer have all of the skills to create a Big Data project?

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Guaranteed Results and Successful Projects

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Use your existing resources more efficiently

Ensure you have effective business and use cases

Build the right teams

Gain deep understanding of technology best practices

What is your team’s probability of success?

Do you know if your team has the right skills to be successful with Big Data?

Making sure the team has all of the right skills is crucial to your success with Big Data. A team missing critical skills or that has an ability gap have extremely low probablitities of success.

Guaranteed Results and Successful Projects

Lorem ipsum dolor sit amet, cons ectetuer adipiscing elit, sed diam nonummy nibh euismod

Use your existing resources more efficiently

Ensure you have effective business and use cases

Build the right teams

Gain deep understanding of technology best practices

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.

Take a look at what other people have said about Data Engineering Teams

Jesse Anderson is an industry veteran who taught and mentored countless data engineering teams – it is wonderful that he finally decided to put his vast knowledge and experience on paper.

GWEN SHAPIRA

Software Engineer, Confluent. Author of Kafka the Definitive Guide and Hadoop Application Architectures

Data Engineering Teams is an invaluable guide whether you are building your first data engineering team or trying to continually improve an established team.

Michael Zargham, PhD

Director, Cadent

Why 85% of Your Internal Big Data Programs Fail or Get Stuck

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:

What’s the difference between the 15% of Big Data initiatives that avoid all the challenges/impediments, and the 85% who don’t?

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.

Custom MapReduce Behind The Dataset

Custom Dataset

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Big Problems From Simple Causes

0

%

Big Data Initiatives

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0

%

Big Data Initiatives

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Technical Training

11

%

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Processing

Massive Datasets

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Six Critical Steps to Success

Simple sounding when you hear them. But they ARE the keys to producing the performance successes required – when integrated and activated together under out expert mentorship.

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

Completely Overhauled in Six Months

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.

“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.

Publicly Traded Digital Marketing Company

Guaranteed Results and Success

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“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.

Publicly Traded Digital Marketing Company

Most Big Data consultancies treat everyone the same.

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From the blog