Local Guides World
Kris K
1 reviews on 1 places
WARNING - DO NOT WASTE A PENNY ON THIER DATA ENGINEERING PROGRAM AS IT'S A RECIPE FOR DISASTER!!!
I completed their Applied Business Intelligence program, however their Data Engineering program wasn't good and this review is purely about DE program.
Background: I worked in traditional ETL space for a while, know data modeling, most of the lingo in the data space.
Most of the companies out there will ask interview questions on SQL, Data Structures and Algorithms for any of the Data Engineering roles. You will not learn any of those skills in this course whatsoever.
The biggest problem I encountered is there's way too many tools squeezed into this DE program. You don't get fair amount of depth and wide knowledge on any of those tools because you're rushing onto the next without having your foundations strong.
We started with Docker, didn't quite learn much and teaching style of that particular instructor didn't help whatsoever to pick up any bits and pieces of docker commands to deploy applications. I remember almost everyone in my class struggled to capture the docker concepts. When instructor asked in our class if we're feeling confident about docker then everyone said, no, we're not and confused. Some even said that they will have too google or take a separate docker class or lessons to pickup those skills.
The data modeling module was good since the instructor had pretty good background, took his own time to explain the concepts in depth. Almost everyone is the class felt happy about that topic. In data engineering, data modeling is crucial part of building infrastructure. Hence, the extra time and effort to get through those concepts are very important for anyone. If the data in the fact table is not stored at the right granularity then entire data model can go wrong. I was happy that they assigned an experience instructor to that module. Forgot his name but he works as an Analytics Engineer.
Spark is another important tool to know in order to survive in data engineering space. I personally think, they should have at least assigned 8-10 weeks for this topic since it's not easy to pick up Spark. There's lot to learn about Spark from in-memory, caching, optimizations, performance tuning, optimization, bucketing. Only some topics were covered to good depth while other areas were skimmed.
There was lot of AWS infrastructure work we had to do like setting up network, IP's, firewalls which could also be done using Terraform (Infrastructure as a Code), but we did lot of manual work.
Overall, it didn't stand up to my expectations and had to drop half-way through the program. Couldn't transfer any of the amount I paid for DE program to another program of theirs and had to pay full fee for the Applied Business Intelligence program.
It wasn't worth paying all that heavy $$$$ to sit through a program that doesn't teach you a lot. I personally don't recommend anyone enrolling into their data engineering program.
I completed their Applied Business Intelligence program, however their Data Engineering program wasn't good and this review is purely about DE program.
Background: I worked in traditional ETL space for a while, know data modeling, most of the lingo in the data space.
Most of the companies out there will ask interview questions on SQL, Data Structures and Algorithms for any of the Data Engineering roles. You will not learn any of those skills in this course whatsoever.
The biggest problem I encountered is there's way too many tools squeezed into this DE program. You don't get fair amount of depth and wide knowledge on any of those tools because you're rushing onto the next without having your foundations strong.
We started with Docker, didn't quite learn much and teaching style of that particular instructor didn't help whatsoever to pick up any bits and pieces of docker commands to deploy applications. I remember almost everyone in my class struggled to capture the docker concepts. When instructor asked in our class if we're feeling confident about docker then everyone said, no, we're not and confused. Some even said that they will have too google or take a separate docker class or lessons to pickup those skills.
The data modeling module was good since the instructor had pretty good background, took his own time to explain the concepts in depth. Almost everyone is the class felt happy about that topic. In data engineering, data modeling is crucial part of building infrastructure. Hence, the extra time and effort to get through those concepts are very important for anyone. If the data in the fact table is not stored at the right granularity then entire data model can go wrong. I was happy that they assigned an experience instructor to that module. Forgot his name but he works as an Analytics Engineer.
Spark is another important tool to know in order to survive in data engineering space. I personally think, they should have at least assigned 8-10 weeks for this topic since it's not easy to pick up Spark. There's lot to learn about Spark from in-memory, caching, optimizations, performance tuning, optimization, bucketing. Only some topics were covered to good depth while other areas were skimmed.
There was lot of AWS infrastructure work we had to do like setting up network, IP's, firewalls which could also be done using Terraform (Infrastructure as a Code), but we did lot of manual work.
Overall, it didn't stand up to my expectations and had to drop half-way through the program. Couldn't transfer any of the amount I paid for DE program to another program of theirs and had to pay full fee for the Applied Business Intelligence program.
It wasn't worth paying all that heavy $$$$ to sit through a program that doesn't teach you a lot. I personally don't recommend anyone enrolling into their data engineering program.