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Increasing volumes of data may require designs that can scale from daily batch to multiple-day micro batch to integration with message queues or real-time change-data-capture for continuous transformation and update. In real life, the slowest part of an ETL process usually occurs in the database load phase.

Databases may perform slowly because they have to take care of concurrency, integrity maintenance, and indices.

Thus, for better performance, it may make sense to employ: Direct path extract method or bulk unload whenever is possible instead of querying the database to reduce the load on source system while getting high speed extract Most of the transformation processing outside of the database Bulk load operations whenever possible Still, even using bulk operations, database access is usually the bottleneck in the ETL process.

Some common methods used to increase performance are: Partition tables and indices : try to keep partitions similar in size watch for null values that can skew the partitioning Do all validation in the ETL layer before the load: disable integrity checking disable constraint Use parallel bulk load when possible — works well when the table is partitioned or there are no indices Note: attempting to do parallel loads into the same table partition usually causes locks — if not on the data rows, then on indices If a requirement exists to do insertions, updates, or deletions, find out which rows should be processed in which way in the ETL layer, and then process these three operations in the database separately; you often can do bulk load for inserts, but updates and deletes commonly go through an API using SQL Whether to do certain operations in the database or outside may involve a trade-off.

For example, removing duplicates using distinct may be slow in the database; thus, it makes sense to do it outside. On the other side, if using distinct significantly x decreases the number of rows to be extracted, then it makes sense to remove duplications as early as possible in the database before unloading data. For example, job "B" cannot start while job "A" is not finished.

One can usually achieve better performance by visualizing all processes on a graph, and trying to reduce the graph making maximum use of parallelism , and making "chains" of consecutive processing as short as possible.

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Again, partitioning of big tables and their indices can really help. Another common issue occurs when the data are spread among several databases, and processing is done in those databases sequentially. Sometimes database replication may be involved as a method of copying data between databases — it can significantly slow down the whole process. The common solution is to reduce the processing graph to only three layers: Sources Central ETL layer Targets This approach allows processing to take maximum advantage of parallelism.

For example, if you need to load data into two databases, you can run the loads in parallel instead of loading into the first — and then replicating into the second. Sometimes processing must take place sequentially. For example, dimensional reference data are needed before one can get and validate the rows for main "fact" tables. Parallel processing[ edit ] A recent [update] development in ETL software is the implementation of parallel processing. It has enabled a number of methods to improve overall performance of ETL when dealing with large volumes of data.

ETL applications implement three main types of parallelism: Data: By splitting a single sequential file into smaller data files to provide parallel access Pipeline : allowing the simultaneous running of several components on the same data stream , e. An additional difficulty comes with making sure that the data being uploaded is relatively consistent.

Because multiple source databases may have different update cycles some may be updated every few minutes, while others may take days or weeks , an ETL system may be required to hold back certain data until all sources are synchronized.

Likewise, where a warehouse may have to be reconciled to the contents in a source system or with the general ledger, establishing synchronization and reconciliation points becomes necessary. Rerunnability, recoverability[ edit ] Data warehousing procedures usually subdivide a big ETL process into smaller pieces running sequentially or in parallel. In case of a failure, having these IDs help to roll back and rerun the failed piece. Best practice also calls for checkpoints, which are states when certain phases of the process are completed.

Once at a checkpoint, it is a good idea to write everything to disk, clean out some temporary files, log the state, etc. The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured, and unstructured data sources.

ETL tools can leverage object-oriented modeling and work with entities' representations persistently stored in a centrally located hub-and-spoke architecture.

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Extract, transform, load

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Data Mining. Concepts and Techniques, 3rd Edition

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A unique key is a column that identifies a given entity, whereas a foreign key is a column in another table that refers to a primary key. Thus, for better performance, it may make sense to employ: Direct path extract method or bulk unload whenever is possible instead of querying the database to reduce the load on source system while getting high speed extract Most of the transformation processing outside of the database Bulk load operations whenever possible Still, even using bulk operations, database access is usually the bottleneck in the ETL process.

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