Google cloud certification data engineer
- 1. Joe Holbrook. Owner of Cloudbursting Corp. Consulting Engineer and Technical Trainer Jacksonville, FL GOOGLE CLOUD CERTIFICATION REVIEW DATA ENGINEER- BETA 04/08/2017
- 2. • A Google Certified Professional - Data Engineer enables data-driven decision making by collecting, transforming, and visualizing data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance, scalability, fidelity, and efficiency of such systems.The Data Engineer also analyzes data to gain insight into business outcomes, builds statistical models to support decision-making, and creates machine learning models to automate and simplify key business processes. • A Google Certified Professional - Data Engineer has demonstrated in our assessment their ability to: • check Build and maintain data structures and databases • check Design data processing systems • check Analyze data and enable machine learning • check Model business processes for analysis and optimization • check Design for reliability • check Visualize data and advocate policy • check Design for security and compliance GOOGLE CLOUD PLATFORM CERTIFIED DATA ENGINEER
- 3. • Here is the page to review. • https://cloud.google.com/certification/data-engineer • Beta Exam Cost $120 • Beta Exam Questions 100 • Beta Exam Time 4 Hours • Beta Exam Case Studies • Test Vendor Very Poor Choice of test vendor. (not a fan) availability/flexibility for testing. Kryterion which has only one place to test if that in many cities is beyond me for a giant like Google. Unlike Pearson and Vue which have a significant network of test centers. Example in Jacksonville there is one location which was a junior college that had a 4 hour/3 day a week testing schedule. I checked Atlanta which had a few more sites…. Unlike VUE/Pearson that can have 20 locations in a city… GCP DATA ENGINEER OVERVIEW
- 4. • Personally I thought the exam was written in a very unprocessed approach and appeared rushed • Did not appear to use best practices in exam development such as Bloom. • Case studies. I did like how they have the case studies listed on Exam Guide so you could review before taking exam and not spend time during exam reviewing. • Exam did have a technical merit but as a routine test developer I see the need for a better exam guide and test JTA to be completed. THOUGHTS ON EXAM
- 5. • Case studies were part of the exam and you needed to review and answer the appropriate solution for the specific questions. Case study had numerous questions similar but had a slight question or answers so you needed to pay attention. • Cloud DataProc - Questions about migrating onsite Hadoop and how Cloud DataProc could help. • Cloud DataFlow - Numerous questions around knowing how Cloud DataFlow fits, know stream vs batch, it’s a managed service so that you don’t need to deal with it, how you can use for ETL. Lastly, you will see several questions related to how Cloud Dataflow can manages services in Google Cloud Storage and Google Cloud Compute Engine. (Yes, know this) THE DATA ENGINEER TECHNICAL REVIEW
- 6. • Pipelines in DataFlow and how you could use graph objects. One question about why you would use JSON or Java related to pipelines. • Storage around every aspect and needed to discern between Nearline and Coldline. Big Data , Regional and Standard storage. Cloud Storage is a must to know. • Hadoop. Numerous questions around HDFS but also when you would need to use approaches like Hive, Sqoop, Oozie with Hadoop. • Stackdriver –not really what I would call Data Engineering but they asked four questions about this Hybrid monitoring service . I remember a few questions focused on how you can debug, monitor and log using Stackdriver. I think they were checking to confirm if you knew how use Stackdriver to help debug source code, etc. THE TECHNICAL REVIEW
- 7. • BigTable- numerous questions about why and how you could use BigTable. Know it’s a high performance NoSQL database service for large analytical and operational workloads. • BigQuery. Tested heavily around data ingestion and availability. Once again they want to confirm you know that it’s a fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. • Cloud SQL. They wanted to confirm you knew it and why over other services like BigTable, BigQuery, etc. TECHNICAL REVIEW
- 8. • Tensorflow. Ok, this area I totally blew at.. I have not been focused on machine learning and AI. There were several questions also focused on “Nueral networks”.. I would highly advise you to review this page. Google seems to expect everyone working on GCP to know machine learning, Cloud ML, Mandlebrot set (Yep). • https://www.tensorflow.org/versions/r0.10/tutorials/ • CloudLab- Another area where I was caught with my pants down. Get warm and fuzzy with this around Cloud Datalab which is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. Numerous question here around how to “Visualize and create models” TECHNICAL REVIEW
- 9. • Pub/Sub. Numerous questions on Pub/Sub and how you would integrate it with other services, how to use to it for publisher apps, messaging, resource types and models. TECHNICAL REVIEW
- 10. • Thank you. • Good Luck on the exam! TECHNICAL REVIEW