Featured Post

8 Ways to Optimize AWS Glue Jobs in a Nutshell

Image
  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

Understand Data power why quality everyone wants

Information and data quality is new service work for data intense companies. I have seen not only in Analytics projects but in Mainframe projects, there is the Data Quality team.

How incorrect data impact on us

Information quality problems and their impact are all around us:
  • A customer does not receive an order because of incorrect shipping information.
  • Products are sold below cost because of wrong discount rates.
  • A manufacturing line is stopped because parts were not ordered—the result of inaccurate inventory information.
  • A well-known U.S. senator is stopped at an airport (twice) because his name is on a government "Do not fly" list.
  • Many communities cannot run an election with results that people trust.
  • Financial reform has created new legislation such as Sarbanes—Oxley. 
Incorrect data leads to many problems. The role of Data Science is to use quality data for effective decisions.

What is information

  1. Information is not simply data, strings of numbers, lists of addresses, or test results stored in a computer. Information is the product of business processes and is continuously used and reused by them. 
  2. It takes human beings to bring information to its real-world context and give it meaning. 
  3. Every day human beings use the information to make decisions, complete transactions and carry out all the other activities that make a business run. Applications come and applications go, but the information in those applications lives on.
  4. Effective business decisions and actions can only be made when based on high-quality information—the key here being effective. Yes, business decisions are based all the time on poor-quality data, but effective business decisions cannot be made with flawed, incomplete, or misleading data. 
  5. People need information they can trust to be correct and current if they are to do the work that furthers business goals and objectives.

Comments

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

Explained Ideal Structure of Python Class

How to Check Kafka Available Brokers