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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 Essential features of HBASE Storage Architecture

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Many analytics prgrammers have confusion about HBASE. The question is if we have HDFS, then why we need HBASE. This post covers how HBASE and HDFS are related in HADOOP big data framework.
HBase is a distributed, versioned, column-oriented, multidimensional storage system, designed for high performance and high availability. To be able to successfully leverage HBase, you first must understand how it is implemented and how it works.
A region server's implementation can have:


HBase is an open source implementation of Google's BigTable architecture. Similar to traditional relational database management systems (RDBMSs), data in HBase is organized in tables. Unlike RDBMSs, however, HBase supports a very loose schema definition, and does not provide any joins, query language, or SQL.


Although HBase does not support real-time joins and queries, batch joins and/or queries via MapReduce can be easily implemented. In fact, they are well-supported by higher-level systems such as Pig and Hive, which use a limited SQL dialect to execute those operations.

The main focus of HBase is on Create, Read, Update, and Delete (CRUD) operations on wide sparse tables. Currently, HBase does not support transactions (but provides limited locking support and some atomic operations) and secondary indexing (several community projects are trying to implement this functionality, but they are not part of the core HBase implementation). As a result, most HBase-based implementations are using highly denormalized data.
Similar to HDFS, HBase implements master/slave (HMaster/region server) architecture.

HBase leverages HDFS for its persistent data storage. This allows HBase to leverage all advanced features that HDFS provides, including checksums, replication, and failover. HBase data management is implemented by distributed region servers, which are managed by HBase master (HMaster).


memstore is HBase's implementation of in-memory data cache, which allows improving the overall performance of HBase by serving as much data as possible directly from memory. The memstore holds in-memory modifications to the store in the form of key/values. A write-ahead-log (WAL) records all changes to the data. This is important in case something happens to the primary storage. If the server crashes, it can effectively replay that log to get everything up to where the server should have been just before the crash. It also means that if writing the record to the WAL fails, the whole operation must be considered a failure.
One of the HBase optimization techniques is disabling the writes to the WAL. This represents a trade-off between performance and reliability. Disabling writes to the WAL prevents recovery when a region server fails before a write operation completes. You should use such an optimization with care, and only in cases when either data loss is acceptable, or a write operation can be "replayed" based on an additional data source.

HFile is a specialized HDFS file format for HBase. The implementation of HFile in a region server is responsible for reading and writing HFiles to and from HDFS.

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