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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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5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.

5 HBase Vs. RDBMS Top Functional Differences

5 HBase Vs. RDBMS Top Functional Differences


Here're the differences unlock now.

Random Accessing


HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database.

Database Rules


RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties.


HBase follows BASE properties and implements complex queries.
Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS.

Storage


From small to medium storage application there is the use of RDBMS that provides the solution with MySQL and PostgreSQL whose size increase with concurrency and performance. 


Codd's rules always need to keep in mind while extending the size of the database in the use of data processing.

Data Integrity


RDBMS focuses on and emphasizes consistency, referential integrity, abstraction from the physical layer, and complex queries through SQL language.

Takeaway

  • There is no single-point failure in HBASE. You always have backup data.
  • The server regions have the flexibility to share or rebalance the load among the servers.
  • Automatic partition helps to distribute its workload among servers. It happens with its in-built feature of HBASE.
  • The cost involved in the maintenance of HBASE is comparatively low.


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