What Is ETL And How the ETL process works?
The too much Data problem
The explosion of data in every aspect of our lives is now a fact. Modern information technology has permeated every facet of this digitally connected world. Recent pandemic further accelerated this already exponential growth with no stopping on the horizon. Every business with any hopes of growth is adopting data driven decision making and the modern data stack sits at the heart of this irreversible change.
The rapid shift to cloud native technologies has provided a number of opportunities to even relatively small businesses to use a number of SaaS tools and applications at nominal prices. Each of these sets of tools and applications have created their own ecosystems with prodigious amounts of data. These data stores become increasingly complex and massive as companies scale and grow fast. They soon result in sluggish data silos, impeding access and data visibility across functions adversely impacting the businesses. In short, what started out as the panacea for more informed and better business decision making becomes an annoying bane.
According to a Gartner report : “Data professionals on average were spending a whopping 78% of their working time on routine data management work and support. That meant they were spending only 22% of their time on more value-adding tasks such as data innovation and the extraction of valuable insights.”
What is ETL
ETL refers to the process of transferring data from source to destination warehouse. It is an acronym for Extract, Transform, and Load. The data is foremost extracted from the sources available, and this data is then transformed into the desired format and then loaded to the Warehouse for further analysis.The ETL process requires active inputs from various stakeholders, including developers, analysts, testers, top executives, and is technically challenging. The industry tends to label the series of steps in ETL as setting up an ETL pipeline analogous to an actual pipeline. However, setting up ETL systems and pipelines is not a simple task due to the inherent challenges of handling multiple data sources each with their own APIs, authentication systems, data formats, rate limitations etc coupled with continually changing business requirements and needs.
The ETL process : A deep dive
As we discussed, ETL is a three-step process to carry out the process of data integration from source to destination. The processes are Data Extraction, Data Transfer, and Data Loading.
In this first step, data is extracted from various sources and all the different formats and collected in the staging area. The staging area is the platform where the data is sorted out before sending it directly to the Data warehouse. Now, there are multiple instances where the data extracted may be corrupted and can ruin the entire structure of the Warehouse if not monitored. There are multiple examples that you can think of under this process. For example, the marketing ETL process, where tons of data is collected or extracted using the process of marketing data integration for the purpose of creating campaigns. All this data is extracted from social media, CRM, ERP tools, etc. By combining all the data, customers are provided with detailed insights, better analysis, and more enhanced results through these campaigns. Similarly, all these processes are carried out with the help of marketing reporting tools to provide a detailed report to the clients by letting them know where their money is being spent. Data extraction from a source can be achieved in three ways which are explained below.
Three ways of typical Data Extraction
- Full Extraction: In this method, data is completely extracted from the source system. The source data will be provided as is and no additional logical information is necessary on the source system. Since it is a complete extraction, there is no need to track the source system for any changes.
- Update Modification: Many ETL tools or databases have a mechanism where the system notifies you when the record or the data has been changed. Such mechanisms support data replication and help to remove it in the next process further.
- Incremental Extraction: In incremental extraction, the changes in source data need to be tracked since the last successful extraction. Only these changes in data will be retrieved and loaded. There can be various ways to detect changes in the source system, maybe from the specific column in the source system that has the last changed timestamp. You can also create a change table in the source system, which keeps track of the changes in the source data. It can also be done via logs if the redo logs are available for the data source. Another method for tracking changes is by implementing triggers in the source database.
The next step is followed by Data transformation. This is the process where all the data collected is suitably formatted before being stored in the data warehouse. A specific set of rules or codes are typically followed to transform the data into a single readable format. Some of the steps to carry out the transformation of Data are explained below.
- Basic Transformation Process: This is the very basic Process of ETL that is carried out. Here the data is extracted, transformed, and then loaded in the Warehouse for further usage. Nothing new here, just the process system is aware of.
- In-warehouse Transformation: Another way to carry out this second step of Data Transformation is through the ELT process. Here data is extracted and kept in the staging area from where it is loaded directly to the Warehouse. After this, the transformation of the data is carried out in the Warehouse.
Both these processes have their advantages and disadvantages. Many systems or organizations carry out the traditional method while some lookout for the ELT method for data Transformations. The detailed differences in both the methods have been explained in our blog. There are several sub-processes involved in Data Transformation:
- Cleaning: Mapping of NULLs to 0 or “Male” to “M” and “Female” to “F,” date format consistency, etc.
- Filtering: Only selective numbers of rows and columns are chosen as required for further analysis.
- Deduplication: Identification and removal of any duplicate records.
- Derivation: Using your data to derive new value from existing data.
- Format Revision: Conversion of data/time format, character set, etc.
- Joining: Linking of data using a set of predefined rules.
- Integration: Reconciling different data names and values for the same data element.
- Verification: Unused/superfluous Data is removed.
All of the above processes prepare the data for better & easier further analysis. Data Transformation, in turn, makes Integration easier making it fully compatible with the next process.
The last step is where you successfully load the data into its new destination i.e., Warehouse. This is a very crucial process that needs proper precision. The data to be uploaded is usually humongous and needs to be handled with a pre-planned loading strategy depending on the type of data. Thus, this process must be fully optimized at the earliest to carry out the operation efficiently. Two basic methods to carry out the Load process are explained below.
- Full Load: Full Load in ETL is loading all the data from the source to the destination. A target table is truncated before loading everything from the source. That’s why this technique is also known as Destructive Load. It is the simplest method to load the data from source to destination. It takes time and labor and may not be the best method to implement as a default.
- Incremental Load: In case the amount of data is too large for it to be loaded in one go or where the actual updated records are very less but the whole data is very huge, we go with the incremental load. The batches of data are recorded and made sure no data is left in the stack, and the process is carried out smoothly. Further, this process, too, has subdivision depending on the increment type viz. Batch Increment Load & Streaming Increment Load.
Challenges in the ETL Process
There are multiple challenges that you may face while trying to carry out the three-step process i.e., Extraction, Transformation, and Loading. This occurs due to improper planning before executing the process. Some of the challenges which may be faced in the three stages are listed below.
- Vast number of incompatible data sources. Today any company, even small to midsize companies are typically working with at least 7-8 different SaaS applications, have their marketing data in different ad platforms and eventually end up needing to move data from 10-12 different data sources each with their own APIs, data formats, protocols, rate limitations any other rule which catches the platform creator’s fancy.
- Constant changes to source APIs / source schema. As the source systems evolve, add new features / functionalities, or adopt new technologies, their APIs evolve, requiring cascading changes to the ETL process. Thus, designing the ETL process is not a one-time engineering effort, but needs regular updates and maintenance.
- Granular control over data being extracted from the sources. Each data source may be exposing a lot of information, some of which may not be useful or may be of a sensitive nature like Personal Identifiable Information (PII) storing which in the data warehouse or data lake may introduce new regulatory complexity / challenge, hence the ETL systems need to have ability to granularly control what information is being extracted from the source. Moreover, transferring all the fields available all the time would incur substantial computational costs.
- Handling ad-hoc data formats and sources. While most systems would have APIs to integrate with, there always would be requirements to ingest data from spreadsheets, CSV files or dumps of json data from files, cloud storages like S3 etc which make the entire process error prone and manual.
- High data transformation requirements and complexity. Most data teams end up underestimating the need for data transformation while conceptualizing the ETL process. Every source system will give data in different formats which would need to be transformed to be of actual use by downstream systems. All of this is not just time consuming but also requires significant resources which need to be catered for at the design stage itself.
- Reliability & Fault Tolerance. Once we set up ETL pipelines and there are downstream systems set up where this information is being utilized for day-to-day functioning and decision making, the data integrity and freshness becomes vital. Hence, the pipeline setup must be reliable, fault tolerant and capable of self-recovery. Designing such systems needs a high degree of technical skill, time, and resources.
- Scalability. Global data growth has been explosive therefore, any ETL systems need to be highly scalable. Even if in the beginning you are expecting a low volume of data, as your company grows, the data volume will grow exponentially.
- Constantly evolving business requirements. As the downstream business teams start employing data to take day to day decisions, their requirements will evolve and the data teams are constantly flooded with requests for more information, adding new metrics, KPIs, providing a different cut / view of the data, all of which keeps the data teams incessantly drowned in drudgery, which is not only not their core job, but also quickly becomes monotonous and tedious.
How to Improve ETL performance?
ETL process, once planned properly, provides good results and carries out smoothly. Only the Load process is carried out in a much slower manner since it involves concurrency, maintenance before loading it in the Warehouse. So there are certain methods that you can follow to improve the performance overall.
- Automate, Automate: It goes without saying that automating your ETL integration processes is key to making them swift and optimal. This can be challenging, especially for teams dealing with legacy infrastructure, tools, and processes, it’s worth repeating how vital automation is. In practice, ETL integration automation means minimizing the role of human operators and relying on tools alone to clean up data, move it through the ETL pipeline and verify the results.
- Partition Tables: There are several large tables that need a thorough partition to smaller tables. It is a proven example, where you need to reduce the size of the tables to smaller ones. As you are aware, each table has its own indices, and the indices are too shallow, allowing the system to collect more data onto them for further implementation. Moreover, partitioning helps in carrying out the process of parallel bulk loading of the data to reduce the time taken.
- Remove Unnecessary Data: It is surely necessary to extract and collect all the data that is available from multiple sources. But instead of sending them to the transformation phase to sort it out, you can do it in the extraction phase itself and remove the unwanted data which are not worthy of loading in the Warehouse.It all depends on the type of business you are running and what kind of data you are looking for. Once you have cleared it up, it will eventually speed up the process to a great extent. Furthermore, there are multiple times that the issue of data duplication occurs, and it can be prevented even before entering into the transformation phase.
- Data Caching: Caching the data is a great thought out method to improve the ETL process. This will increase system performance faster than before. It only depends on the type of hardware you are using the space it has.Data caching is the need of the hour not only because it helps the system access the necessary data anytime when needed, but also it allows access to this data directly from the memory where it is stored.
- Incremental Loading of Data: As we have already discussed, the slowest process is the Load process since huge chunks of data need to be loaded in the Warehouse. Which is why incremental loading can serve a huge deal to speed up the process. Systems can easily divide the data into smaller sections and send them one by one.It can be either in small volume or larger volume, depending on the organizations and usage of the data. If the company needs a large chunk of data, then the loading needs to be done for large files and vice versa.
- Using Parallel Bulk Load: Instead of carrying the loading process serially, the process can be made to happen parallelly. This method is only possible and works well when the tables have been partitioned into multiple smaller tables with indices.Sometime it may not work if the machine is working on 100% CPU functions. This can be improved if the CPU is upgraded, which will eventually speed up the system.
- IoT Data Integration: Using future technology to carry out the Process of IoT data integration will increase productivity at a rapid pace. With the use of the Internet of things in your business activities to sort out all the data you have collected in multiple ways, such as improving customer experience, predictive analysis, etc. For this, there are many data integration tools using IoT as their base technology to collect, sort, and transform the data for further use.
- Reverse ETL: Activation of data from the data warehouse to business tools is increasingly being adopted by all businesses committed to the modern data stack. Data driven decision making dictates that ubiquitous data visibility across all functional teams is the new normal. Thus, any ETL strategy would be suboptimal if it doesn't plan for this inevitable paradigm change. For a definitive guide on the reverse ETL process see our blog.
DataChannel – An integrated ETL & Reverse ETL solution
- 100+ Data Sources. DataChannel’s ever-expanding list of supported data sources includes all popular advertising, marketing, CRM, financial, and eCommerce platforms and apps along with support for ad-hoc files, google sheets, cloud storages, relational databases, and ingestion of real-time data using webhooks. If we do not have the integration you need, reach out to our team and we will build it for you for free.
- Powerful scheduling and orchestration features with granular control over scheduling down to the exact minute.
- Granular control over what data to move. Unlike most tools which are highly opinionated and dictate what data they would move, we allow you the ability to choose down to field level what data you need. If you need to add another dimension or metric down the line, our easy to use UI lets you do that in a single click without any breaking changes to your downstream process.
- Extensive Logging, fault tolerance and automated recovery allows for dependable and reliable pipelines. If we are unable to recover, the extensive notifications will alert you via slack, app and email for taking appropriate action.
- Built to scale at an affordable cost. Our best in class platform is built with all ETL best practices built to handle billions of rows of data and will scale with your business when you need them to, while allowing you to only pay for what you use today.
- Get started in minutes. Get started in minutes with our self-serve UI or with the help of our on-boarding experts who can guide you through the process. We provide extensive documentation support and content to guide you all the way.
- Managed Data Warehouse. While cloud data warehouses offer immense flexibility and opportunity, managing them can be a hassle without the right team and resources. If you do not want the trouble of managing them in-house, use our managed warehouse offering and get started today. Whenever you feel you are ready to do it in-house, simply configure your own warehouse and direct pipelines to it.
- Activate your data with Reverse ETL. Be future-ready and don’t let your data sit idle in the data warehouse or stay limited to your BI dashboards. The unidimensional approach toward data management is now undergoing a paradigm change. Instead, use DataChannel’s reverse ETL offering to send data to the tools your business teams use every day. Set up alerts & notifications on top of your data warehouse and sync customer data across all platforms converting your data warehouse into a powerful CDP (Customer Data Platform). You can even preview the data without ever leaving the platform.