Data is an important component for every business which makes Database ETL integral to Data Analytics. It is a rich source of information that can help businesses make sound decisions. However, for a business to extract information from data, it must analyze it. The problem is that most data sources are not optimized for analytics.
This means that businesses should extract data from such sources and move it to a tool that is optimized for analytics. In most cases, this is a Data Warehouse like BigQuery or Snowflake. The ETL process helps businesses integrate data from multiple sources into a Data Warehouse.
Table of Contents
- What is ETL?
- How does ETL Work?
- Difference between ETL and ELT
- ETL Extract
- ETL Transform
- ETL Load
- Why is ETL Important?
- ETL Tools
- What does the future hold for ETL?
- What are the Common ETL Challenges?
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- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
What is ETL?
ETL refers to the three steps (Extract, Transform, Load) used to integrate data from multiple sources. It’s the common process used to build a data warehouse. During the ETL process, data is taken (extracted) from the source system, converted (transformed) into a format that is easy to analyze, and stored (loaded) into a data warehouse or another system. The exact ETL steps may differ from one tool to another, but the end result is the same.
ETL solves two major problems to enable better analytics:
1. Data analysis can be performed in an environment that is optimized for that purpose: Transactional database management systems like PostgreSQL and MySQL are good for processing transactional workloads. They are good at reading and updating single rows of data with low latency. However, they are not good at conducting large-scale analytics across huge datasets.
2. Cross-domain analysis: When business leaders join data from multiple sources, they can answer deeper business problems. This demand becomes more urgent as businesses become more complex and deploy systems in the cloud.
For more details, you can refer to our blog.
How does ETL Work?
Previously, this process would have extracted data from one or more OLTP (Online Transactional Processing) databases. OLTP Applications generally consist of a high volume of transactional data that needs transformation and integration to operational data. This is needed because it can come in handy for Data Analysis and Business Intelligence.
The data gets extracted on to a staging area, that serves as a storage location that sits between the data source and the data target. Within that staging area, these tools can modify the data by cleansing, joining, and otherwise optimizing it for analysis.
This tool can then be leveraged to load this data into a Decision Support System (DSS) database, where BI teams can run queries and show results and reports to business users to help them make decisions and strategies.
However, since the traditional tools still require a considerable amount of labor from data professionals, this is where the modern tools jump into the fray. You can easily analyze data from pre-calculated OLAP summaries, which helps ease and speed up the process.
Difference between ETL and ELT
Traditionally, Database ETL processes extracted and transformed data before they were loaded into the Data Warehouse. However, most of the businesses now use Cloud-based Data Warehouses to store all their operational data for analytical purposes as opposed to setting up their own On-Premise Data Warehouse. Even through businesses can still use the traditional Database ETL methods for Cloud-based systems, ELT is preferred over ETL due to its vast array of advantages.
In terms of Data Management and Workload, Cloud-based systems are deemed way more scalable in terms of storage and processing as compared to the traditional On-premise Data Warehouses. It is quite unlikely that the ETL process would be able to leverage the numerous improvements offered by a Cloud-based Data Warehouse. Since the ETL process continues to treat the Cloud-based Data Warehouses like the traditional Data Warehouses, it results in the same performance bottlenecks. From this observation, you can infer that switching to Cloud-based systems for ETL processes provides no supplementary value.
ELT, on the other hand, has been built to leverage all the best features of a Cloud-based Data Warehouse. This includes massively Parallel Processing, ability to swiftly tear down jobs, spin up jobs, and elastic scalability. Therefore, all the necessary data is taken from the sources and loaded into the Cloud without any further modifications. Next, the ELT process will leverage the high processing power of the Cloud to execute the requisite transformations on the data as and when needed.
Despite ELT processes having a clear edge over ETL, ELT lags behind when it comes to storage. ELT processes need much more storage space because the raw data needs to be stored without any amendments. So, the necessary data would have to be taken from the raw data storage and transformed every time you need it for analysis.
This is the first step of the Database ETL process. During this ETL phase, someone in the organization identifies the desired data sources and the columns, rows, and fields to be extracted from the sources.
The sources in the Database ETL process normally include:
- Transactional databases are hosted in the cloud or on-site.
- Hosted business applications.
The business leaders should estimate the data volumes to be extracted from each data source. The data should be extracted in a way that it does not have a negative effect on the response times or source systems.
Data extraction is done using any of the following ways:
1. Update notification
You can extract data from a source system when a record is changed. Most Database systems such as Oracle and MS SQL Server have a log-based Change Data Capture mechanism to support database replication, and most SaaS applications have webhooks which offer the same functionality.
2. Incremental extraction
Some Database systems such as MongoDB and PostgreSQL cannot provide a notification when a record is changed, but they can identify the records that have changed and provide an extract of those records. Changes made to the source data are tracked since the last successful extraction so that you don’t extract all the data each time there is a change.
During the subsequent Database ETL steps, the system will want to identify the changes and propagate them down. However, incremental extraction may not be able to identify the deleted records in the source data.
3. Full extraction
Some systems such as web pages cannot identify the data that has changed, so the only way to get the data out of the system is by reloading all data. This data extraction method requires that you keep a copy of the last extract to check the new records. Since this method involves the transfer of data in high volumes, you should only use it as the last resort, and only for small tables.
Data extracted from the source server is raw and not usable in its original form. Hence, there is a need to cleanse, map, and transform it. This is the phase of the Database ETL process that adds value and changes data for the generation of insightful BI reports.
There are two approaches to the Database ETL Transformation process:
1. Multistage Data Transformation
In this approach, the extracted data is moved to a staging area where transformations are done before loading it into the warehouse.
2. In-warehouse Data Transformation
In this approach, the data is loaded into the Analytics Warehouse, in which the transformations are done.
ETL Transformation Types
There are different types of transformations that are applied to data during the Database ETL process.
Here are the common ones:
This involves mapping NULL values to 0, “Male” to “M”, “Female” to “F”, implementing date format consistency, etc.
This involves identifying and removing any duplicate records.
3. Format Revision
Unit of measurement conversion, character set conversion, date/time conversion, etc.
Selecting only specific rows and/or columns.
Linking data from multiple sources, for example, Facebook Ads and Google Ads data.
Splitting a single column into multiple columns.
This is the last step of the Database ETL process and it involves loading data into the Data Warehouse. In most cases, huge data volumes need to be loaded into the Data Warehouse within short periods of time. Hence, the load process must be optimized for performance.
In case of a load failure, recovery mechanisms should be employed to restart from the point of failure without loss of data integrity. There are two methods of Database ETL that you can use to load data into a Data Warehouse:
1. Full Load
A whole data pump is done the first time a data source is loaded into the Data Warehouse.
2. Incremental Load
Delta between source and target data is dumped at regular intervals. The last extract date is recorded so that only records added after that date are loaded.
There are two types of incremental loads depending on the volume of data that you’re loading:
- Streaming incremental loads- good for loading small volumes of data.
- Batch incremental loads- good for loading large volumes of data.
Why is ETL Important?
Here are a few reasons why ETL is still considered a pivotal process by enterprises:
- Database ETL generates an easy path for businesses to analyze and provide the data relevantly on their assigned initiatives.
- Database ETL also has the potential to provide historical context for your organization when used in tandem with the data at rest within the Data Warehouse.
- Database ETL also provides support for the upcoming interaction requirements.
- You can leverage Database ETL to migrate data without any technical skills. Therefore, it can help improve the productivity of the team dramatically.
- Database ETL is deemed as one of the most essential tools for an organization with its Data Reporting, Data Warehousing, and analytics tools.
You’ve three options when it comes to Database ETL tools:
- Purchase a commercial Database ETL tool.
- Use an open-source ETL tool.
- Write your own Database ETL script.
If you choose to write your own Database ETL script, you can choose Python, SQL, etc. However, Python is the most popular choice. It’s also a good choice for data scientists and engineers who need to do the Database ETL process for themselves. If you wish to take a look at the best ETL Tools available in the marketplace, you can go through this blog.
What does the future hold for ETL?
Here is what the future holds for Database ETL:
- Exponential Data Growth: IoT data will continue to grow at an exponential rate and play a pivotal role in our lives. Based on the recent statistics, we will continue to move away from traditional Data Warehouses and adopt Cloud-based Warehouses instead. This increases the importance of cloud-native tools to transform, manage, lead, and integrate data in the cloud.
- More Machine Learning and Artificial Intelligence: Preparing the data for Machine Learning and Artificial Intelligence will become a more critical use case because digital assistance and the next-best action technologies continue to expand on a humongous scale.
- Data Democratization: Data will become more ubiquitous going forward. Businesses need and want their employees to execute data-driven decisions. This means centralizing data and employing tools that decrease the need for manual processes to make way for increased insight. This also means that businesses would be needing different kinds of tools for different use cases. Pipeline tools for business users, batch and streaming capabilities based on the demand for real-time information along with full data transformation capabilities within IT. As organizations continue to become more self-service based, they will continue to stand out as opposed to competitors who refuse to adapt.
What are the Common ETL Challenges?
Here are the challenges that you’ll encounter during the Database ETL process:
- Scalability: Scalability is pivotal to the functioning of a modern tool. The amount of data being collated by businesses is only going to go up. You might be resorting to batch migration for now, but as your business evolves, you might need to adopt Streaming Replication. This is where the Cloud jumps into the fray.
- Accurate Transformation of Data: Another challenge you might face is accurate and complete data transformation. Coding or manual changes and a failure to test and plan before running a Database ETL job can sometimes introduce faults, loading replicas, including missing data, among other issues. A Database ETL tool can help you reduce the need to hand-code and decrease the occurrence of errors drastically. You can also use Data Accuracy testing to identify inconsistencies and duplicates. You can use monitoring features to identify instances where you are dealing with incompatible data types among other Data Management issues.
- Diverse Data Sources: Data continues to grow in volume and complexity. One company might be handling diverse data from multiple data sources that consist of structured and semi-structured sources, streaming sources, FLAT files, etc. Some of this data can be easily transformed in batches, while for others streaming transformation might be suggested. Handling each type of data in the most practical and effective manner might pose an enormous challenge.
What is the Primary Difference between Database Testing and ETL Testing?
ETL Testing can be executed by a user for the purposes of forecasting, analytical reporting, and information. Database Testing, on the other hand, is executed to integrate and validate the data. ETL Testing is generally carried out on the data within a Data Warehouse system as opposed to Database Testing which is commonly performed on transactional systems.
What are the types of Databases in ETL?
The different types of databases that can be leveraged in Database ETL are as follows:
- NoSQL Databases
- Cloud Databases
- Relational Databases
- Wide-column Databases
- Columnar Databases
- Key-value Databases
- Object-oriented Databases
- Graph Databases
- Hierarchical Databases
- Document Databases
- Time Series Databases
How many steps are there in an ETL Process?
The 5 steps that comprise the ETL process are as follows:
- Extract: In this step, you extract raw data from multiple disparate sources, which is then moved to a temporary staging data repository.
- Clean: In this step, the raw data gets cleaned, ensuring the quality of data before transformation.
- Transform: In this step, the data is converted and structured to match the correct target source.
- Load: Here, the structured data is loaded into a Data Warehouse so that it can be properly analyzed.
- Analyze: In this step, Big Data is processed within the Data Warehouse, allowing the business to gain insight from the properly configured data.
This is what you’ve learnt in this article:
- What is the Database ETL process.
- What is involved in each phase of the ETL process.
- How to choose an ETL tool.
If you’re looking for a more straightforward solution, you can use Hevo Data – a No Code Data pipeline to build perform Database ETL in an instant.
Hevo has pre-built integrations with 100+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo! sSign up here for a 14-Day Free Trial!Visit our Website to Explore Hevo
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