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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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How to Show Data Science Project in Resume Correctly

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The data scientist resume should have mentioned the project correctly. Here are my ideas on how to show the project in the resume.

Resume: How to Show Data Science Project


How to Show Data Science Project?

1. Preparation of Resume. 

The first step for an interview for any project is you need Resume. You need to tell clearly about your resume. 


2. Answering about Project. 

In interviews, you will be asked questions about your project. So the second step is you need to be in a position to explain the project.


3. Answering your Project Role. 

The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have a 100% chance to shortlist your resume.

Based on your experience your resume can be 1 page or 2 pages. 

4. Technologies in the Resume.

In interviews, again they will be asked how you used different tools to complete your data science project. So, you need to be in a position to explain how you used different options present in the tools. 

Sometimes, in interviews, they may ask about the specific role in Tools specifically you used. You should be in a position to answer these questions too.

How to Prepare Resume. 

  1. Write a clear description.
  2. Write a specific role.
  3. Explain Tools. 
  4. Write about data flow.

1. Write a Clear Description.  In any data science project, you will find a few things like Client name, the expectation of the client, and what you are going to deliver. These things you need to present clearly in your Resume. 


2. Write Specific Roles. Roles. To convince your interviewer, you need to tell about your team roles and your specific role. In general, you can find the following roles:
  • Architect.
  • Data scientist.
  • Business Analyst.
  • Development team.
  • Testing Team.
  • Integration testing team.
  • Production release team.

3. Explain Tools. You need to present all the Tools your project is using and your specific tools. Then, in the face-to-face interview, you need to tell what options you used to achieve what.

For example, I used some integration tool, to receive data to the development region, and to send out after unit testing. If you explain, these key points, I can say, 100% sure, you will be selected.

4. Write about data flow. You need to explain how data is coming, is it in the sequential data set or document data. Something you need to tell clearly.

You also need to tell, after unit testing, which forms you will send the data to the next region. If you know this flow correctly, then you can convince easily your interviewer.

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