In computing, extract, transform, load (ETL) is a three-phase process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collated from one or more sources and it can also be output to one or more destinations. ETL processing is typically executed using software applications but it can also be done manually by system operators. ETL software typically automates the entire process and can be run manually or on recurring schedules either as single jobs or aggregated into a batch of jobs.
A properly designed ETL system extracts data from source systems and enforces data type and data validity standards and ensures it conforms structurally to the requirements of the output. Some ETL systems can also deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions.[1]
The ETL process is often used in data warehousing.[2] ETL systems commonly integrate data from multiple applications (systems), typically developed and supported by different vendors or hosted on separate computer hardware. The separate systems containing the original data are frequently managed and operated by different stakeholders. For example, a cost accounting system may combine data from payroll, sales, and purchasing.
Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming it into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational data store, a data mart, data lake or a data warehouse.[3][4]
Extract
ETL processing involves extracting the data from the source system(s). In many cases, this represents the most important aspect of ETL, since extracting data correctly sets the stage for the success of subsequent processes. Most data-warehousing projects combine data from different source systems. Each separate system may also use a different data organization and/or format. Common data-source formats include relational databases, flat-file databases, XML, and JSON, but may also include non-relational database structures such as IBM Information Management System or other data structures such as Virtual Storage Access Method (VSAM) or Indexed Sequential Access Method (ISAM), or even formats fetched from outside sources by means such as a web crawler or data scraping. The streaming of the extracted data source and loading on-the-fly to the destination database is another way of performing ETL when no intermediate data storage is required.
An intrinsic part of the extraction involves data validation to confirm whether the data pulled from the sources has the correct/expected values in a given domain (such as a pattern/default or list of values). If the data fails the validation rules, it is rejected entirely or in part. The rejected data is ideally reported back to the source system for further analysis to identify and to rectify incorrect records or perform data wrangling.
Transform
In the data transformation stage, a series of rules or functions are applied to the extracted data in order to prepare it for loading into the end target.
An important function of transformation is data cleansing, which aims to pass only "proper" data to the target. The challenge when different systems interact is in the relevant systems' interfacing and communicating. Character sets that may be available in one system may not be so in others.
In other cases, one or more of the following transformation types may be required to meet the business and technical needs of the server or data warehouse:
Selecting only certain columns to load: (or selecting null columns not to load). For example, if the source data has three columns (aka "attributes"), roll_no, age, and salary, then the selection may take only roll_no and salary. Or, the selection mechanism may ignore all those records where salary is not present (salary = null).
Translating coded values: (e.g., if the source system codes male as "1" and female as "2", but the warehouse codes male as "M" and female as "F")
Encoding free-form values: (e.g., mapping "Male" to "M")
Deriving a new calculated value: (e.g., sale_amount = qty * unit_price)
Sorting or ordering the data based on a list of columns to improve search performance
Joining data from multiple sources (e.g., lookup, merge) and deduplicating the data
Aggregating (for example, rollup — summarizing multiple rows of data — total sales for each store, and for each region, etc.)
Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
Splitting a column into multiple columns (e.g., converting a comma-separated list, specified as a string in one column, into individual values in different columns)
Disaggregating repeating columns
Looking up and validating the relevant data from tables or referential files
Applying any form of data validation; failed validation may result in a full rejection of the data, partial rejection, or no rejection at all, and thus none, some, or all of the data is handed over to the next step depending on the rule design and exception handling; many of the above transformations may result in exceptions, e.g., when a code translation parses an unknown code in the extracted data
Load
The load phase loads the data into the end target, which can be any data store including a simple delimited flat file or a data warehouse. Depending on the requirements of the organization, this process varies widely. Some data warehouses may overwrite existing information with cumulative information; updating extracted data is frequently done on a daily, weekly, or monthly basis. Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals — for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data. However, the entry of data for any one year window is made in a historical manner. The timing and scope to replace or append are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the data warehouse. As the load phase interacts with a database, the constraints defined in the database schema — as well as in triggers activated upon data load — apply (for example, uniqueness, referential integrity, mandatory fields), which also contribute to the overall data quality performance of the ETL process.
For example, a financial institution might have information on a customer in several departments and each department might have that customer's information listed in a different way. The membership department might list the customer by name, whereas the accounting department might list the customer by number. ETL can bundle all of these data elements and consolidate them into a uniform presentation, such as for storing in a database or data warehouse.
Another way that companies use ETL is to move information to another application permanently. For instance, the new application might use another database vendor and most likely a very different database schema. ETL can be used to transform the data into a format suitable for the new application to use.
An example would be an expense and cost recovery system such as used by accountants, consultants, and law firms. The data usually ends up in the time and billing system, although some businesses may also utilize the raw data for employee productivity reports to Human Resources (personnel dept.) or equipment usage reports to Facilities Management.
Real-life ETL cycle
The typical real-life ETL cycle consists of the following execution steps:
ETL processes can involve considerable complexity, and significant operational problems can occur with improperly designed ETL systems.
The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifications, leading to an amendment of validation rules explicitly and implicitly implemented in the ETL process.
Data warehouses are typically assembled from a variety of data sources with different formats and purposes. As such, ETL is a key process to bring all the data together in a standard, homogeneous environment.
Design analysis[5] should establish the scalability of an ETL system across the lifetime of its usage — including understanding the volumes of data that must be processed within service level agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data warehouses with tens of terabytes of data. 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.
Performance
ETL vendors benchmark their record-systems at multiple TB (terabytes) per hour (or ~1 GB per second) using powerful servers with multiple CPUs, multiple hard drives, multiple gigabit-network connections, and much memory.
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 ...) in the target database tables during the load
Disable triggers (disable trigger ...) in the target database tables during the load: simulate their effect as a separate step
Generate IDs in the ETL layer (not in the database)
Drop the indices (on a table or partition) before the load - and recreate them after the load (SQL: drop index ...; create index ...)
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 (x100) 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.
A common source of problems in ETL is a big number of dependencies among ETL jobs. 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. 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
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.g. looking up a value on record 1 at the same time as adding two fields on record 2
Component: The simultaneous running of multiple processes on different data streams in the same job, e.g. sorting one input file while removing duplicates on another file
All three types of parallelism usually operate combined in a single job or task.
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
Data warehousing procedures usually subdivide a big ETL process into smaller pieces running sequentially or in parallel. To keep track of data flows, it makes sense to tag each data row with "row_id", and tag each piece of the process with "run_id". 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.
Virtual ETL
As of 2010[update], data virtualization had begun to advance ETL processing. 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. Such a collection that contains representations of the entities or objects gathered from the data sources for ETL processing is called a metadata repository and it can reside in memory or be made persistent. By using a persistent metadata repository, ETL tools can transition from one-time projects to persistent middleware, performing data harmonization and data profiling consistently and in near-real time.
Dealing with keys
Unique keys play an important part in all relational databases, as they tie everything together. 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. Keys can comprise several columns, in which case they are composite keys. In many cases, the primary key is an auto-generated integer that has no meaning for the business entity being represented, but solely exists for the purpose of the relational database - commonly referred to as a surrogate key.
As there is usually more than one data source getting loaded into the warehouse, the keys are an important concern to be addressed. For example: customers might be represented in several data sources, with their Social Security number as the primary key in one source, their phone number in another, and a surrogate in the third. Yet a data warehouse may require the consolidation of all the customer information into one dimension.
A recommended way to deal with the concern involves adding a warehouse surrogate key, which is used as a foreign key from the fact table.[6]
Usually, updates occur to a dimension's source data, which obviously must be reflected in the data warehouse.
If the primary key of the source data is required for reporting, the dimension already contains that piece of information for each row. If the source data uses a surrogate key, the warehouse must keep track of it even though it is never used in queries or reports; it is done by creating a lookup table that contains the warehouse surrogate key and the originating key.[7] This way, the dimension is not polluted with surrogates from various source systems, while the ability to update is preserved.
The lookup table is used in different ways depending on the nature of the source data. There are 5 types to consider;[7] three are included here:
Type 1
The dimension row is simply updated to match the current state of the source system; the warehouse does not capture history; the lookup table is used to identify the dimension row to update or overwrite
Type 2
A new dimension row is added with the new state of the source system; a new surrogate key is assigned; source key is no longer unique in the lookup table
Fully logged
A new dimension row is added with the new state of the source system, while the previous dimension row is updated to reflect it is no longer active and time of deactivation.
Tools
An established ETL framework may improve connectivity and scalability.[citation needed] A good ETL tool must be able to communicate with the many different relational databases and read the various file formats used throughout an organization. ETL tools have started to migrate into enterprise application integration, or even enterprise service bus, systems that now cover much more than just the extraction, transformation, and loading of data. Many ETL vendors now have data profiling, data quality, and metadata capabilities. A common use case for ETL tools include converting CSV files to formats readable by relational databases. A typical translation of millions of records is facilitated by ETL tools that enable users to input csv-like data feeds/files and import them into a database with as little code as possible.
ETL tools are typically used by a broad range of professionals — from students in computer science looking to quickly import large data sets to database architects in charge of company account management, ETL tools have become a convenient tool that can be relied on to get maximum performance. ETL tools in most cases contain a GUI that helps users conveniently transform data, using a visual data mapper, as opposed to writing large programs to parse files and modify data types.
While ETL tools have traditionally been for developers and IT staff, research firm Gartner wrote that the new trend is to provide these capabilities to business users so they can themselves create connections and data integrations when needed, rather than going to the IT staff.[8] Gartner refers to these non-technical users as Citizen Integrators.[9]
Extract, load, transform (ELT) is a variant of ETL where the extracted data is loaded into the target system first.[10] The architecture for the analytics pipeline shall also consider where to cleanse and enrich data[10] as well as how to conform dimensions.[1] Some of the benefits of an ELT process include speed and the ability to more easily handle both unstructured and structured data.[11]
Ralph Kimball and Joe Caserta's book The Data Warehouse ETL Toolkit, (Wiley, 2004), which is used as a textbook for courses teaching ETL processes in data warehousing, addressed this issue.[12]
Cloud-based data warehouses like Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics and Snowflake Inc. have been able to provide highly scalable computing power. This lets businesses forgo preload transformations and replicate raw data into their data warehouses, where it can transform them as needed using SQL.
After having used ELT, data may be processed further and stored in a data mart.[13]
Most data integration tools skew towards ETL, while ELT is popular in database and data warehouse appliances. Similarly, it is possible to perform TEL (Transform, Extract, Load) where data is first transformed on a blockchain (as a way of recording changes to data, e.g., token burning) before extracting and loading into another data store.[14]
In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating reports. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.
Online analytical processing, or OLAP, is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.
Data migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one computer storage system to another. Additionally, the validation of migrated data for completeness and the decommissioning of legacy data storage are considered part of the entire data migration process. Data migration is a key consideration for any system implementation, upgrade, or consolidation, and it is typically performed in such a way as to be as automated as possible, freeing up human resources from tedious tasks. Data migration occurs for a variety of reasons, including server or storage equipment replacements, maintenance or upgrades, application migration, website consolidation, disaster recovery, and data center relocation.
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:
A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time.
Microsoft SQL Server Integration Services (SSIS) is a component of the Microsoft SQL Server database software that can be used to perform a broad range of data migration tasks.
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. The result is a delta-driven dataset.
Data cleansing or data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall.
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration.
A slowly changing dimension (SCD) in data management and data warehousing is a dimension which contains relatively static data which can change slowly but unpredictably, rather than according to a regular schedule. Some examples of typical slowly changing dimensions are entities such as names of geographical locations, customers, or products.
Spatial extract, transform, load, also known as geospatial transformation and load (GTL), provides the data processing functionality of traditional extract, transform, load (ETL) software, but with a primary focus on the ability to manage spatial data.
A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns, and more options for data compression. However, they are typically less efficient for inserting new data.
A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
In the data warehouse practice of extract, transform, load (ETL), an early fact or early-arriving fact, also known as late-arriving dimension or late-arriving data, denotes the detection of a dimensional natural key during fact table source loading, prior to the assignment of a corresponding primary key or surrogate key in the dimension table. Hence, the fact which cites the dimension arrives early, relative to the definition of the dimension value.
Datavault or data vault modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed and resilience to change as well as emphasizing the need to trace where all the data in the database came from. This means that every row in a data vault must be accompanied by record source and load date attributes, enabling an auditor to trace values back to the source. The concept was published in 2000 by Dan Linstedt.
A staging area, or landing zone, is an intermediate storage area used for data processing during the extract, transform and load (ETL) process. The data staging area sits between the data source(s) and the data target(s), which are often data warehouses, data marts, or other data repositories.
Innovative Routines International (IRI), Inc. is an American software company first known for bringing mainframe sort merge functionality into open systems. IRI was the first vendor to develop a commercial replacement for the Unix sort command, and combine data transformation and reporting in Unix batch processing environments. In 2007, IRI's coroutine sort ("CoSort") became the first product to collate and convert multi-gigabyte XML and LDIF files, join and lookup across multiple files, and apply role-based data privacy functions for fields within sensitive files.
Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.
The term is used for two different things:
In computer science, in-memory processing (PIM) is a computer architecture in which data operations are available directly on the data memory, rather than having to be transferred to CPU registers first. This may improve the power usage and performance of moving data between the processor and the main memory.
In software engineering, in-memory processing is a software architecture where a database is kept entirely in random-access memory (RAM) or flash memory so that usual accesses, in particular read or query operations, do not require access to disk storage. This may allow faster data operations such as "joins", and faster reporting and decision-making in business.
The following is provided as an overview of and topical guide to databases:
References
1 2 3 Ralph., Kimball (2004). The data warehouse ETL toolkit: practical techniques for extracting, cleaning, conforming, and delivering data. Caserta, Joe, 1965-. Indianapolis, IN: Wiley. ISBN978-0764579233. OCLC57301227.
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