ELT Pipelines: A Comprehensive Guide for 2022

• April 19th, 2022

elt pipeline: FI

A Data Pipeline is a system for transporting data from one location (the source) to another (the destination) (such as a data warehouse). Data is transformed and optimized along the way, and it eventually reaches a state that can be analyzed and used to develop business insights.

Extract, Load, Transform (ELT) is a data integration process that involves transferring raw data from a source server to a target server’s data system (such as a Data Warehouse or Data Lake) and then preparing the data for downstream uses.

This article extensively describes ELT Pipelines. It also explains the need for ELT pipelines, a few ELT Pipeline tools, and the Challenges of Leveraging ELT Pipelines.

Table Of Contents

What is Data Pipeline?

A Data Pipeline is a set of steps for processing data. The data is ingested at the start of the pipeline if it has not yet been loaded into the data platform. Then there’s a series of steps, each of which produces an output that becomes the input for the next step. This will go on until the pipeline is finished. Independent steps may be run in parallel in some cases.

Organizations are moving data between more and more applications as they look to build applications with small code bases that serve a very specific purpose (these types of applications are called “microservices“). This makes the efficiency of Data Pipelines a critical consideration in their planning and development. Data generated by a single source system or application may feed multiple Data Pipelines, and those pipelines may be dependent on the outputs of multiple other pipelines or applications.

Consider a single social media comment. This event could provide information for a real-time report that counts social media mentions, a sentiment analysis app. This returns a positive, negative, or neutral result, or an app that plots each mention on a world map.

Even though all of the data comes from the same source, each of these applications is built on its own set of Data Pipelines that must run smoothly before the end-user sees the result.

Data Transformation, Augmentation, Enrichment, Filtering, Grouping, Aggregation, and the application of algorithms to that data are all common steps in Data Pipelines.

A Data Pipeline is a series of steps that ingest raw data from various sources and transport it to a storage and analysis location. Filtering and features that provide resiliency against failure may also be included in a pipeline.

Best ELT Data Pipelines for 2022 

In this article, we discuss various data management tools you could use to achieve your data goals. Hevo Data fits the list as an ETL and Data Integration Tool that helps you load data from 100+ sources(including 40+ sources) into one data warehouse of your choice.

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Here’s a list of the Best ELT Tools for 2022:

What is ELT Pipeline?

ETL (extract, transform, and load) processes were once used to load data into a data warehouse in traditional Data Pipelines. Analysts must predict the data models and insights they will use ahead of time when transforming before loading. This means that data engineers and analysts must frequently design and build complex processes and workflows ahead of time to use data, then redesign and rebuild them as reporting requirements change.

The new wave of extracting, loading, and transforming data (ELT) allows analysts to use data in its original form while transforming it with cloud-based technologies to extract insights with no limits. This new approach to data integration speeds up the data ingestion process and lowers the entry barrier to comprehensive analytics significantly.

ELT Pipeline refers to the process of extracting data from source systems, loading it into a Data Warehouse environment, and then transforming it using in-database operations like SQL. It requires the ability to store large amounts of raw data in the beginning.

Cloud data warehouses, which store and process data at a low cost, are changing the way businesses manage their analytical data. The transition from on-premises servers to cloud data warehouses is causing an ETL to ELT shift.

Key Benefits of ELT Pipeline

There are several advantages to using ELT Pipeline and cloud-based data warehouses and data lakes over using ETL and on-premises hardware.

  • Time to Value: ELT Pipeline typically has a shorter time to value, which means that business intelligence is available sooner. ETL, on the other hand, necessitates a time-consuming and resource-intensive transformation step before loading or integrating data.
  • Scalability: Cloud data warehouses that are designed to autoscale in the face of increased processing loads are used in conjunction with ELT tools. Older generations of on-premise data warehouses require organizations to order, install, and configure new hardware, whereas cloud platforms allow for almost unlimited scale in seconds or minutes.
  • Flexibility: Many BI tools determine schema on reading and perform transformations on-demand, allowing you to replicate raw data into your data warehouse or data lake and transform it as needed.
  • Future-Proofed Data Sets: Although ELT implementations can be used directly for data warehousing systems, it is frequently used in the Data Lake approach, which collects data from a variety of sources. This, in combination with the separation of the transformation process, makes future warehouse structure changes easier.
  • Leveraging the Latest Technologies: ELT solutions take advantage of new technologies to drive enterprise-wide improvements, security, and compliance. ELT Pipeline also makes use of modern cloud data warehouses and big data processing frameworks’ built-in capabilities.
  • Simplifying Management: ELT Pipeline separates the loading and transformation processes, reducing interdependencies, reducing risk, and simplifying project management.

Working of ELT Pipeline

The processes used by a Data Pipeline to replicate data from a source system into a target system, such as a cloud data warehouse, are known as ELT.

  • Extraction: The first step entails transferring data from the source system to the target system.
  • Loading: The pipeline replicates data from the source into the target system, which could be a Data Warehouse or a Data Lake, during the loading step.
  • Transformation: Organizations can run whatever transformations they require once the data is in the target system. Raw Data is frequently transformed in a variety of ways by organizations to be used with various tools or business processes.

ELT Pipeline is a modern take on the older extract, transform, and load process, in which data is transformed before being loaded. When transformations are run before the load phase, the data replication process becomes more complex.

Before loading data into a destination, ETL tools require processing engines to run transformations. Businesses, on the other hand, use the destination’s processing engines to efficiently transform data within the target system with ELT Pipeline. The data loading process is streamlined as a result of the removal of an intermediate step.

Use Case of ELT Pipeline

ETL is the best process when a destination requires a specific data format because it transforms data before loading it. This could be due to a mismatch in supported data types between the source and destination, a destination’s limited ability to quickly scale processing, or security restrictions that prevent the destination from storing raw data.

ELT Pipeline is a better approach when the destination is a cloud-native data warehouse such as Amazon Redshift, Google BigQuery, Snowflake, or Microsoft Azure SQL Data Warehouse. Organizations can transform their raw data at any time, as needed for their use case, rather than as a Data Pipeline step.

Why do you need ELT Pipelines?

Traditional ETL architectures are monolithic in nature, frequently used to connect only to schema-based data sources, and they have little or no room to process data flowing at high speeds. It is nearly impossible for ETL tools to fetch the entire or a portion of the source data into memory, apply transformations, and then load it to the warehouse when businesses deal with high velocity and veracity of data.

 You now have a variety of data sources in modern applications, ranging from structured schema-based SQL sources to unstructured NoSQL databases.

These various data sources rarely have the same schema, and implementing the logic in a traditional ETL tool becomes more difficult or time-consuming with each new source added.

The adoption of cloud data warehouses or data lakes by organizations is an important factor that leads to the implementation of ELT systems. Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) or Amazon Redshift are two examples of cloud data warehouses. These cloud data warehouses use MPP (Massively Parallel Processing) architecture and can be set up in a matter of minutes. Snowflake is another example of a cloud data warehouse where all of the infrastructures are managed for you, and all you have to do is design your business logic.

These cloud data warehouses also support the columnar store, which comes in handy when analyzing large datasets with petabytes of data. Unlike traditional row-wise data stores, data in a columnar store is stored in columns. Furthermore, cloud data warehouses are set up to run on multiple nodes with a mix of RAM and SSDs to support data processing at high speeds. These are just a few of the reasons why a cloud data warehouse is ideal for transforming and analyzing data on-the-fly without degrading query performance.

ELT Vs ETL: Critical Differences 

ELT is a process that involves extracting data from disparate sources, loading it into a target datastore, typically a data warehouse, and transforming it—performing such actions as changing the data types or applying calculations. ELT Pipeline is a great choice for a business that wants to prioritize ingestion speed.

ETL is a process that involves extracting data from disparate sources, and validating and transforming the data before loading it into a target data store. It is especially useful when there is no consistency in the data coming from the source systems which requires you to standardize (validate/transform) all the data coming in before loading into a database, usually an MPP Data Warehouse.

  • Order Of Events: The distinction here is based on the order of events. In ETL you apply transformations to the data while it’s being moved while in an ELT context you are transforming the data after it has been moved to your data warehouse. 
  • Transformations: ETL has the advantage of landing data in its finished and transformed state. But as the volume of data increases, the ability of the ETL tool to load the data in a timely fashion is compromised. In this scenario, ELT has the advantage of leveraging a target MPP database to drive transformations. 
  • Tools and Technologies: As a developer, you probably hear a lot about new ELT tools and technologies that promise to increase the speed at which you can migrate data between different data stores, as well as ones that can increase the consistency of your data in transit. Your challenge is to wade through these emerging technologies and determine which ones actually can keep up with your specific requirements for the projects that you are currently working on. No doubt, you are aware of the various ELT tools available in the market. However, you might not know which tool can provide the best value to you and your projects. This article seeks to separate the wheat from the chaff and explores the different types of ELT tools available, and provides guidelines that can help you with real-world ELT pipelines.

Key ELT Pipeline Tools

Some famous ELT Pipeline tools are explained below:

Hevo

Hevo Data, a No-code Data Pipeline, offers a fully managed one-stop solution for all your ELT needs. Expediting your Data Replication Process, Hevo quickly transfers data from 100+ sources (Including 40+Free Sources) to your Desired Data Warehouse/ Destination of your choice. Once the data is moved to a Data Warehouse, users can clean and organize data for further analysis and visual analysis. Hevo is fully managed and completely automates the process without having to write a single line of code.

Hevo’s ELT approach of data transformations helps move your data from source to destination warehouse using our Models and Workflows approach. Hevo offers an intuitive and easy-to-use Model interface where you can apply transformations to the data till you reach the final data model that is required for your business intelligence and reporting requirements. Workflows further extend the functionality of the Models and provide you a powerful way to derive insights by joining multiple Models for you to further perform deeper analysis.

Features 

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • 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.
  • Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Best-Suited Use Case

Hevo is both an ELT and ETL platform and allows businesses to have analytics-ready data at their fingertips at all times. With powerful automation and scheduling abilities, teams can rapidly access and transform the data that they need, so more time is spent on analysis.

Pricing

Hevo Data provides users with three different subscription offerings, namely, Free, Starter, and Business. The free plan houses support for unlimited free data sources, allowing users to load their data to a data warehouse/desired destination for absolutely no cost! The basic Starter plan is available at $249/month and can be scaled up as per your data requirements. You can also opt for the Business plan and get a tailor-made plan devised exclusively for your business.

Hevo’s pricing is based on the number of events taking place, and the user can choose a plan as per requirements. Explore the pricing details here.

Hevo can assist you in quickly extracting data from 100+ Sources to your target destination and lets you perform powerful transformations with a no-code, easy-to-setup interface. Try our 14-day full feature access free trial.

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Blendo

Blendo is known as one of the best ELT tools enabling customers to centralize all of their different datasets and data sources into a central location. They are in the business of building new connectors and maintaining the ones already created. Over the years, they’ve grown to over 40+ integrations. They provide a fast way to replicate your application, databases, events, and files into the fully managed and elastic cloud warehouses such as BigQuery, and Redshift.

Features

  • Fully managed Data Pipelines as a service
  • Limited maintenance and configuration
  • Automated schema migrations
  • 40+ connectors and counting

Best-Suited Use Case

It is a good choice for businesses that want to move data from Facebook Ads, Google Ads, Google Analytics, Hubspot, LinkedIn Ads, Mailchimp, MySQL, Salesforce, Shopify, and Stripe to Amazon Redshift, Google BigQuery, Microsoft SQL Server, Snowflake, PostgreSQL, and Panoply.

Pricing

Prices start at $150 for the starter package which has standard connectors and $500 for the advanced package which has over 150 pipelines.

Matillion

Matillion is one of the best ELT Tools that is built specifically for Amazon Redshift, Azure Synapse, Google BigQuery, and Snowflake. Matillion houses ELT architecture. It sits between your raw data sources (internal, external, and third-party data) and your BI and Analytics tools.

Matillion ELT Pipeline takes away the compute-intensive activity of loading data from your on-premise server that is perhaps already under pressure with its regular transaction-handling role and instead leaves this process to the data warehouses that tend to have an infinite amount of parallel processing resources.

Features

  • Pay-as-you-go model with no long-term financial commitments.
  • Scalable built to take advantage of the power and features of your data warehouse.
  • Makes complex tasks simple with an intuitive UI and approach to data transformation.
  • Automated data workflows. 
  • Drag-and-drop browser-based UI so you can build your ELT jobs in minutes.

Best-Suited Use Case

If you’re using Amazon Simple Storage Service (S3), Amazon Redshift, Azure Synapse, Google BigQuery, or Snowflake for your data Warehousing needs, then Matillion is a good choice for your use case. However, keep in mind that Matillion doesn’t support ELT load jobs to other Data Warehouses—it is designed specifically for those solutions. 

Drawbacks

  • One of the drawbacks of leveraging Matillion for your business use case is its steep learning curve understanding and implementing complex features becomes challenging for new development teams.
  • You can sometimes encounter validation failure in scheduled jobs for no discernable reason.
  • Clustering is not supported which means large load jobs can take up a long time to process or even lead to OOM errors.
  • Integration with version control systems is a complex undertaking.

Pricing

Now Matillion is making their ELT platform available starting at $1.37 per hour which translates to $12K annually assuming 24/7 usage. For larger teams and higher performance production workloads, there is a plan that starts at $5.48 per hour or $48K annually. They also offer a free 14-day trial

Challenges of Leveraging ELT Pipeline

Every organization relies on the right tools and expertise to complete a task correctly. As with any task, early mistakes in the production process compound as the project grows, and there are a few common pitfalls that can jeopardize any ELT architecture:

  • Security Gaps: Moving petabytes of data and making it available to all applications and users poses security concerns. A secure design approach integrates security at all levels of the organization, ensuring that data warehouses are not infected by corrupted or compromised data sets.
  • Compliance Complacency: Organizations are under more pressure to perform mandatory audits and prove standards are met as compliance frameworks such as HIPAA, PCI, and the GDPR grow in importance. To avoid running afoul of national and international regulations, any ELT approach should be designed with compliance in mind.
  • Resource Bloating: The benefits of having data warehouses to mine for business intelligence come with one obvious drawback: maintaining all of that data. Cloud providers and pay-as-you-go pricing have made big data more accessible than ever before, but even tiered storage pricing can become costly without a strategy for managing and limiting the never-ending expansion of working data sets.

Conclusion

This article talks about ELT Pipeline in detail. It also discusses a few ELT Pipeline tools and their features.

Integrating and analyzing your data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo is a No-code Data Pipeline and has awesome 100+ pre-built integrations that you can choose from. Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data and create your Dashboards. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure.

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You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

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