Extract, transform, load - Wikipedia

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Conventional ETL architecture
Data transformation
Concepts
Metadata
Data element
Data mapping
Data migration
Data transformation
Model transformation
Macro
Preprocessor
Transformation languages
ATL
AWK
MOFM2T
QVT
XML languages
Techniques and transforms
Identity transform
Data refinement
Applications
Data conversion
Data migration
Data integration
Extract, transform, load
(ETL)
Web template system
Related
Data wrangling
Transformation languages
Extract, transform, load
ETL
) is a three-phase
computing
process where data is
extracted
from an input source,
transformed
(including
cleaning
), and
loaded
into an output data container. The data can be collected 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.
The ETL process is often used in
data warehousing
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.
ETL and its variant ELT (extract, load, transform), are increasingly used in cloud-based data warehousing. Applications involve not only batch processing, but also real-time streaming.
Phases
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Extract
edit
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
edit
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 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. For example 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. For example, mapping "Male" to "M".
Deriving a new calculated value. For example, 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.
Generating
surrogate-key
values.
Transposing
or
pivoting
(turning multiple columns into multiple rows or vice versa).
Splitting a column into multiple columns. For example, 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
edit
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.
Additional phases
edit
A real-life ETL cycle may consist of additional execution steps, for example:
Cycle initiation (pre-ETL: Ensure all ETL-relevant systems are available, functional, and have no issues that would suddenly halt an hour into ETL, such as valid
B2B
compute/memory/storage subscriptions and API keys, satisfactory budget for estimated time/money, no higher-priority tasks)
Build
reference data
(i.e., linking (
not hard-copying
) to reliable historic baselines)
Extract (from data sources)
Validate
(ensure data can
eventually
be useful; data is not 100% garbage)
Transform (
clean
, apply
business rules
, check for
data integrity
, create
aggregates
or disaggregates)
Stage (load into
staging
tables, if used)
Audit reports
(for example, on compliance with business rules. Also, in case of failure, helps to diagnose/repair)
Publish (to target tables)
Archive
(Extreme, space-efficient compression for very rarely used, old data. Situationally, can be
lossy
Design challenges
edit
ETL processes can involve considerable complexity, and significant operational problems can occur with improperly designed ETL systems.
Data variations
edit
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
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.
Uniqueness of keys
edit
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.
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.
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;
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.
Performance
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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 computing
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Some ETL software implementations include
parallel processing
. This enables 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.
Failure recovery
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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.
Implementations
edit
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.
Gartner refers to these non-technical users as Citizen Integrators.
Variations
edit
In online transaction processing
edit
ETL diagram in the context of
online transaction processing
In
online transaction processing
(OLTP) applications, changes from individual OLTP instances are detected and logged into a snapshot, or batch, of updates. An ETL instance can be used to periodically collect all of these batches, transform them into a common format, and load them into a data lake or warehouse.
Virtual ETL
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Data virtualization
can be used 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.
Extract, load, transform (ELT)
edit
Main article:
Extract, load, transform
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.
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
See also
edit
Architectural pattern
(EA reference architecture)
CMS Pipelines
Create, read, update and delete
(CRUD)
Data cleansing
Data integration
Data mart
Data mesh
, a domain-oriented data architecture
Data migration
Data transformation (computing)
Electronic data interchange
(EDI)
Enterprise architecture
Legal Electronic Data Exchange Standard
(LEDES)
Metadata discovery
Online analytical processing
(OLAP)
Online transaction processing
(OLTP)
Spatial ETL
References
edit
Ralph., Kimball (2004).
The data warehouse ETL toolkit : practical techniques for extracting, cleaning, conforming, and delivering data
. Caserta, Joe, 1965-. Indianapolis, IN: Wiley.
ISBN
978-0764579233
OCLC
57301227
Denney, MJ (2016).
"Validating the extract, transform, load process used to populate a large clinical research database"
International Journal of Medical Informatics
94
271–
4.
doi
10.1016/j.ijmedinf.2016.07.009
PMC
5556907
PMID
27506144
Zhao, Shirley (2017-10-20).
"What is ETL? (Extract, Transform, Load) | Experian"
Experian Data Quality
. Retrieved
2018-12-12
Pott, Trevor (4 June 2018).
"Extract, transform, load? More like extremely tough to load, amirite?"
The Register
. Retrieved
2018-12-12
Theodorou, Vasileios (2017). "Frequent patterns in ETL workflows: An empirical approach".
Data & Knowledge Engineering
112
1–
16.
doi
10.1016/j.datak.2017.08.004
hdl
2117/110172
Kimball, The Data Warehouse Lifecycle Toolkit, p. 332
Golfarelli/Rizzi, Data Warehouse Design, p. 291
"The Inexorable Rise of Self Service Data Integration"
Gartner
. 22 May 2015
. Retrieved
31 January
2016
"Embrace the Citizen Integrator"
Gartner
. Retrieved
September 29,
2021
Amazon Web Services, Data Warehousing on AWS, p. 9
Mishra, Tanya (2023-09-02).
"ETL vs ELT: Meaning, Major Differences & Examples"
Analytics Insight
. Retrieved
2024-01-30
"The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data [Book]"
Amazon Web Services, Data Warehousing on AWS, 2016, p. 10
Bandara, H. M. N. Dilum; Xu, Xiwei; Weber, Ingo (2020). "Patterns for Blockchain Data Migration".
Proceedings of the European Conference on Pattern Languages of Programs 2020
. pp.
1–
19.
arXiv
1906.00239
doi
10.1145/3424771.3424796
ISBN
9781450377690
S2CID
219956181
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