Most enterprises are leveraging vast reserves of data to improve their business insights and decision-making. However, as companies manage larger stores of data and move more and more information from operational databases to data warehouses, it creates an ever-mounting threat of data breaches.
To combat these threats, most enterprises implement data governance and data management policies that comply with a range of regulations and standards such as GDPR, SOC2, HIPAA, CCPA, as well as the company’s internal data governance rules. For most enterprises, a key part of the data governance process involves the encryption or removal of sensitive data before moving information to a data warehouse.
This is where the concept of “ETLG” (Extract, Transform, Load for Data Governance) comes into play. At Integrate.io, we use the term ETLG to refer to the process of performing the minimum-required, lightweight transformations on your data (for data governance purposes) before loading it into the destination data warehouse. If more complex transformations are required, you can wait to perform them in the warehouse itself. In this respect, the ETLG strategy allows you to satisfy data governance requirements while also allowing you to rapidly ingest data without needing to worry about designing and coding complex transformations beforehand.
In this article, we’ll look at the concept of ETLG, and how it helps businesses satisfy their data governance and compliance rules while achieving the speed and flexibility of rapid data ingestion. But first, we’ll take a look at data governance and data compliance in the context of data integration.
Table of Contents
- Overview of Data Governance and Data Management
- How ETLG Satisfies Data Governance Needs While Achieving Rapid Data Ingestion
- Build an ETLG Strategy with Integrate.io
Overview of Data Governance and Data Management
Data governance and data management are two separate concepts that go hand in hand. Let’s take a look at each one separately:
1) Data Governance
Data governance refers to an organization’s rules, policies, and procedures that ensure the safe and correct usage and storage of information. A data governance policy codifies the data-related rules and requirements an organization will follow, in addition to clarifying the organization’s own internal data security standards.
A data governance policy does not implement any security rules, policies, or procedures. It simply codifies them. In doing so, a data governance policy usually answers the following questions:
- Which employees can access and read specific information?
- Which employees can access and edit or change specific information?
- What rules and processes does your organization adhere to when storing data?
- How long will your organization store different types of data?
- What policies and practices will ensure that stored data is secure?
- How will your organization mitigate the risks associated with storing sensitive information?
It’s important to note that the rules of a data governance policy could require masking, encrypting, or removing sensitive data (such as PII and PHI) before passing information to a data warehouse for BI analysis. This is because industry standards and government regulations, such as GDPR, SOC2, HIPAA, CCPA, etc., may require these security-related, pre-load data transformations.
Since this kind of data encryption policy involves pre-load transformations, implementation needs to occur through an ETL (Extract, Transform, Load) process. An ETL process can encrypt/redact sensitive information immediately after extracting it from the source, and before loading it into a destination data warehouse. In contrast, an ELT process cannot satisfy these pre-load transformation requirements. That’s because all ELT transformations occur after loading the data into the data warehouse. For this reason, when a data governance policy requires pre-load transformations to protect PII/PHI information (which is extremely common), organizations may not be able to implement an ELT workflow, even if such a workflow suits their purposes.
The Unified Stack for Modern Data Teams
2) Data Management
Data management refers to the implementation and execution of the data governance rules, policies, and procedures. The data management process might involve the following tasks:
- Setting up role-based access control that enforces who can access, read, or edit specific information types.
- Configuring all databases and data warehouses, they adhere to the data storage rules laid out in the data governance plan.
- Configuring and continually managing systems, so they follow industry rules, government regulations, and your organization’s internal data security standards.
- Policing and monitoring the safety of stored data and identifying and resolving any safety risks.
- Setting up a master data monitoring system, which allows the data management team to view all data stats throughout the organization.
Ultimately, data management monitors and carries out the above tasks to ensure that the handling of all data – from the moment the data is created to the moment it is destroyed – adheres to the data governance policy. For example, when it comes to moving data from an operational database to a data warehouse for BI purposes, it’s the data management process that configures, implements, and monitors the data integration workflow according to the governance policy.
In adhering to the data governance policy, some data management processes involve the encryption or pseudonymization of PHI/PII data before loading it into the data warehouse via (1) an ETL process alone, or (2) a mix of ETL and ELT that involves lightweight, pre-load transformations (ETL), and saves more complex transformations to occur in the data warehouse later (ELT).
How ETLG Satisfies Data Governance Needs While Achieving Rapid Data Ingestion
The Unified Stack for Modern Data Teams
ETLG (Extract, Transform, Load for Data Governance) allows you to reap the advantages of both pre-load ETL transformations and post-load ELT transformations. Essentially, ETLG empowers your data management processes to satisfy the pre-load PII/PHI encryption rules in your data governance policy – yet still ingest data rapidly, allowing you to benefit from the incredible data ingestion speeds and flexible business logic of an ELT approach to data integration.
Essentially, the ETLG workflow might look like this:
- Extract: Pull the data from the source and load it into a staging area.
- Pre-Load Transformations for Security: Perform light transformations on the data to remove or encrypt PII/PHI and other confidential information, and perform simple formatting functions for data governance/management purposes.
- Load: Load the lightly-transformed secure information into the destination.
- Post-Load, more complex transformations: If further transformations are desired, use the processing power of the data warehouse to perform more complex transformations.
In a traditional ETL workflow, all transformations must occur before loading. If transformations are numerous and complex, that can delay data ingestion for certain cases. On the other hand, an ETLG process allows you to quickly perform lightweight pre-load transformations to satisfy data management and data compliance requirements and save the rest of the transformations for later. This offers greater speed and agility when it comes to integrating data from a new source into a data warehouse.
With ETLG, you can also save more compute-heavy transformations for later so they occur within the warehouse itself. This offers greater flexibility to change your data integration process and business logic as needed. It also allows you to benefit from the tremendous power and speed of using a cloud-based data warehouse system to process transactions.
Build an ETLG Strategy with Integrate.io
The Unified Stack for Modern Data Teams
Now that you’ve learned how ETLG can support your data governance and data management requirements while still allowing you to reap the benefits of ELT, you might want to try building an ETLG workflow yourself. One of the easiest and most affordable ways to build an ETLG strategy is to add the ETL-as-Service platform, Integrate.io, to your data integration stack.
Integrate.io is a powerful, easy-to-use platform that allows anyone, regardless of their data engineering skill level, to quickly build sophisticated ETL processes without writing a single line of code. As an essential data management tool, Integrate.io can perform a handful of lightweight, high-speed transformations that mask, encrypt or remove sensitive data (like PHI, PII) before moving data from one system to another. In this way, Integrate.io can help you adhere to the terms of your data governance policy while keeping pre-load transformations light, fast, and easy for anyone to set up. If and when necessary, you can always perform additional transformations within the destination data warehouse.
If you’d like to try Integrate.io for yourself, contact our team to find out how to get a demo or 14-day trial of the platform.
FAQs
What is ETL in data governance? ›
Data extract, transform, load (ETL) means transforming data to load in an organization's data warehouse. ETLs often are automated processes once they are built and usually require preparation and pipeline work.
What is the importance of ETL in data governance? ›ETL, or extract, transform, and load, is the most popular method of integrating data. It consolidates data from several disparate source systems into a data warehouse or another destination system with the purpose of improving data access.
What is data security in data governance? ›Data security is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.
What are the five steps of the ETL process? ›The 5 steps of the ETL process are: extract, clean, transform, load, and analyze. Of the 5, extract, transform, and load are the most important process steps.
What are the 3 layers in ETL? ›ETL stands for Extract, Transform, and Load.
What are the three key components of data governance? ›A good data governance program typically includes the steering committee with three main groups: data owners, data stewards, and data custodians. The three positions all work together to create the policies, process, and procedures for governing data, especially the reference data and master data elements.
What is the main purpose of data governance? ›Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.
What are the five areas of data governance? ›- Accountability. Accountability is of the utmost importance in any successful data governance process. ...
- Standardized Rules and Regulations. ...
- Data Stewardship. ...
- Data Quality Standards. ...
- Transparency.
Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.
What are data governance tools? ›A data governance tool is defined as a tool that aids in the process of creating and maintaining a structured set of policies, procedures, and protocols that control how an organization's data is stored, used, and managed.
What are the 3 types of data security? ›
There are three core elements to data security that all organizations should adhere to: Confidentiality, Integrity, and Availability.
What is an example of data governance? ›An example of data governance is when an organization adopts a data governance initiative in order to: define data models, distribute roles and responsibilities regarding the use of data, retention of old and new data — particularly sensitive data — create data standards, implement protection and establish security in ...
How do you ensure good data governance? ›- Think with the big picture in mind, but start small. ...
- Build a business case. ...
- Metrics and more metrics. ...
- Communicate early and often. ...
- Account for the fact data governance is a marathon, not a sprint. ...
- Identify related roles and responsibilities.
ETL stands for Extract, Transform and Load. An ETL tool extracts the data from different RDBMS source systems, transforms the data like applying calculations, concatenate, etc. and then load the data to Data Warehouse system. The data is loaded in the DW system in the form of dimension and fact tables.
What are ETL principles? ›Three points need to drive ETL design
If data is to be extracted from a source, focus on extracting that data; do not attempt to bring in data from several other sources and mash up the results at the same time. The more tasks to be performed, the higher the level of complexity within the process.
This blog post covers the top 16 ETL (Extract, Transform, Load) tools for organizations, like Talend Open Studio, Oracle Data Integrate, and Hadoop.
How many types of ETL are there? ›Types of ETL Tools. ETL tools can be grouped into four categories based on their infrastructure and supporting organization or vendor. These categories — enterprise-grade, open-source, cloud-based, and custom ETL tools — are defined below.
Which tool is used for ETL Testing? ›ETL Validator is an ETL testing automation tool developed by Datagaps which helps in automating the ETL/ELT validation during data migration and data warehouse projects.
What is a main component of data governance? ›Data governance programs need to establish key data processes for data management. Common policies include data issue tracking/resolution, data quality monitoring, data sharing, data lineage tracking, impact analysis, automated data quality testing, and many others.
What are data governance rules? ›A data governance framework is the collection of rules, processes, and role delegations that ensure privacy and compliance in an organization's enterprise data management. Every organization is guided by certain business drivers — key factors or processes that are critical to the continued success of the business.
What makes data governance successful? ›
A successful data governance program will require time and resources – especially during the initial stages where the working structure and processes are still being defined and implemented. Provide council members with the necessary support so they can dedicate the time to make the program a priority.
What do you expect the biggest challenge to be in data governance? ›Common Challenges of Data Governance include: Lack of Data Leadership. Understanding Business Value of Data Governance. Recognizing the Need / Pain Caused by Data.
Who is responsible for data governance? ›A well-designed, high-quality data governance program usually includes a responsible governance team, a steering committee that serves as the governing body, and a group of data stewards. Data stewards refer to an oversight role in data governance within an organization.
What is the best data governance tool? ›- Comparison Table For Data Governance Tools.
- #1) OvalEdge.
- #2) Integrate.io.
- #3) Alation.
- #4) Dataddo.
- #5) Atlan.
- #6) Collibra.
- #7) IBM Data Governance.
- Accidental Exposure. ...
- Phishing and Other Social Engineering Attacks. ...
- Insider Threats. ...
- Ransomware. ...
- Data Loss in the Cloud. ...
- SQL Injection. ...
- Data Discovery and Classification. ...
- Data Masking.
These can be adopted by commercial organizations, but, most often, we find four levels, Restricted, Confidential, Internal, Public.
What are the 7 kinds of security? ›There are essentially seven issues associated human security. These are economic security, food security, health security environmental security, personal security, community security, and political security.
What is the first step in data governance? ›Determining the strategy for having an effective data governance team in an organization is the first step in developing a data governance structure. This strategy can be started by writing a data governance charter with the assistance of stakeholders and those involved in the project who work at the company.
What is ETL and why it is used? ›ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.
What is ETL explain in brief? ›ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.
What is an example of ETL? ›
As the data is loaded into the data warehouse or data lake, the ETL tool sets the stage for long-term analysis and usage of such data. Examples include banking data, insurance claims, and retail sales history.
What is ETL and SQL? ›The SQL Server ETL (Extraction, Transformation, and Loading) process is especially useful when there is no consistency in the data coming from the source systems. When faced with this predicament, you will want to standardize (validate/transform) all the data coming in first before loading it into a data warehouse.
Which ETL tool is widely used? ›Informatica PowerCenter is one of the best ETL tools on the market. It has a wide range of connectors for cloud data warehouses and lakes, including AWS, Azure, Google Cloud, and SalesForce. Its low- and no-code tools are designed to save time and simplify workflows.
What are the three most common transformations in ETL processes? ›- Cleaning: Mapping NULL to 0 or "Male" to "M" and "Female" to "F," date format consistency, etc.
- Deduplication: Identifying and removing duplicate records.
- Format revision: Character set conversion, unit of measurement conversion, date/time conversion, etc.
ETL stands for extract, transform, and load. It is a data integration process that extracts data from various data sources, transforms it into a single, consistent data store, and finally loads it into the data warehouse system. It provides the foundation for data analytics and machine learning in an organization.
What are features of ETL? ›What are the Features of ETL Software? ETL software typically includes features such as data extraction from various sources, data transformation, data loading into a data warehouse or repository, data mapping and transformation, data quality and validation, scheduling and automation, and reporting and visualization.
What is a real time example of ETL process? ›A credit card fraud detection application is another simple example of streaming ETL in action. When you swipe your credit card, the transaction data is sent to, or extracted by, the fraud detection application.
What are modern ETL tools? ›Modern ETL tools are capable of importing and exporting both structured data and unstructured data from virtually any source, from spreadsheets to IoT sensors. They can also scale in a timely, cost-effective way to accommodate fluctuating workloads.
What is ETL skill? ›The abbreviation ETL stands for Extract, Transform, and Load. It's a method of moving data from various sources into a data warehouse. It is one of the crucial elements of business intelligence. An ETL developer is an IT specialist who designs data storage systems.
Which SQL is used in ETL Testing? ›ETL testing is a multi-level, data-centric process. It uses complex SQL queries to access, extract, transform and load millions of records contained in various source systems into a target data warehouse.
Is Microsoft SQL an ETL tool? ›
Microsoft SQL Server Integration Services (SSIS) is a platform for building high-performance data integration solutions, including extraction, transformation, and load (ETL) packages for data warehousing.