Why Is ELT Better For Cloud Data Warehousing?
Feb 23, 2021
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Why Is ELT Better For Cloud Data Warehousing?

Introduction:

Modern data warehousing such as cloud and other hybrid environments provides integrated machine learning solutions to store data that enables both customer insights and business intelligence (BI) to help make faster business decisions. Data warehousing refers to collecting data from several different sources, such as business applications, mobile data, and social media. Then this data is used to deliver valuable business insights and analytical reports.

The heterogeneous data obtained from various sources are first cleansed and then organized into a consolidated format in the data warehouse. Enterprises and organizations use data warehousing tools and Database Management Systems (DBMS) to access the data stored on the warehouse servers for supporting their business operational decisions.

The market growth of data warehousing is attributed to factors such as the increasing amount of data generated by enterprises and a growing need for BI to gain a competitive advantage. The huge volumes of data produced by different businesses are exerting tremendous pressure on their existing resources, thus forcing them to adopt data warehousing solutions for flexible, efficient, and scalable storage. This data can be leveraged using advanced data mining and BI tools to provide valuable business insights to users for strengthening their customer retention, increasing their operational efficiency, help in better decision-making, and increase their revenue streams.

Storing data on-premises can become quite expensive if the computing power and storage have different scalability. Data-driven organizations are the early birds to swap the traditional on-premises with cloud data warehouses that are more agile and flexible. That?s because the latter can instantly scale themselves to deliver high or low computing needs as required, thus, making them highly cost-effective.

Such companies are finding that their ETL (Extract Transform Load) tools are less adaptable to the hybrid environments, and demand more upgrades, ultimately makes them turn the costlier investment in the long run. The scalability and computing power of ETL is proving to be Achilles' heel. Cisco reports that 94% of all workloads will run in some form of the cloud environment by 2021.[1]

Numerous business enterprises are now considering the benefits of cloud data warehousing which include on-demand computing, multiple data type support, flexible pricing models, integrated BI tools, and unlimited storage. SMEs (Small and Medium Enterprises) are rapidly adopting the cloud warehousing model due to low infrastructure needs and affordable costs. As the last decade saw a rapid growth of cloud adoption rates across many industries. Today, cloud data storage accounts for 45% of all enterprise data, and the number could grow to 53% by Q2 of 2021. [2]

The available cloud data warehouses that allow companies to manage their analytical data by storing and cost-effectively processing data are changing. The shift from the on-premises servers toward the could data warehouses is sparking a shift from ETL to ELT (Extract Load Transform). ELT is an alternative technique compared to the traditional ETL, as this process involves pushing the transformation component of the process to the target database for better performance. This capability is quite useful to process the massive data sets required for big data analytics and business intelligence (BI).

Let us now discuss an efficient process to move and transform data for analysis, crucial for business growth and innovation in this data-driven world.

Loading a data warehouse can be an extremely time-consuming process. ELT is the process of extracting data from one or multiple sources, load it into a target data warehouse, instead of transforming the data before it is written.

The ELT process involves streamlining the tasks of modern data warehousing and managing big data so that businesses can focus on data mining for actionable insights. ELT capitalize on the target system that does the data transformation. With ELT needing only the raw and unprepared data, this approach needs fewer remote sources than other techniques.

ELT reduces the time, data spends in transit and boosts efficiency, as it takes advantage of the processing capability that is built into a data storage infrastructure. Though this process has been in practice for some time now, it is now gaining popularity with the more widespread use of Hadoop and cloud-native data lakes.

What is ELT?

It stands for extract, load, and transform- the processes a data pipeline uses for replicating the data from a source system into a target system such as a cloud data warehouse.

Extraction is the first step in which data is copied from the source system.
Loading is the next step, where the pipeline replicates data from the source into the target system that could be a data lake or a data warehouse.
Transformation is where once the data is in the target system, the organizations can run whatever transformations they need. Usually, they will transform the raw data in different ways for use with different tools or business processes.

ELT And Its Infrastructure

ELT is a modern variation on the older ETL process in which the transformations take place before loading the data. Running the transformations before the load phase results in a more complex data replication process. ETL tools need processing engines to run the transformations before loading the data into a destination. While with ELT, businesses use the processing engines in the destinations for efficiently transforming data within the target system. So, the removal of an intermediate step streamlines the data loading process.

Use Case of ELT

As the ETL transforms data before the loading stage, it is an ideal process when the destination needs a specific data format. This could include when there?s a misalignment in the supported data types between the source and destination, or due to limited ability to scale processing in a destination quickly, or security restrictions that make it impossible to store the raw data in a destination.

Liv Up, the Brazilian food tech start-up provides an example of the effectiveness of ELT in data warehouses. The company integrated data from a variety of sources, such as Google Analytics, MongoDB, and Zendesk into its data warehouse. Though the process was effective in the past it was a bit cumbersome.

Data from MongoDB was quite challenging as it needed to translate NoSQL data into a relational data structure. The company took about a month to write the code for their data pipeline with traditional ETL. That?s when they started to look for reducing the time to value and get their data to its destination more quickly.

So, the company turned to Stitch, a cloud-first, developer-focused platform that helps to expedite the data replication process. Acquired by Talend in November 2018, the company operates as an independent business unit. Hundreds of data teams rely on them to securely and reliably move their data from the SaaS (Software-as-a-Service) tools and databases into the data lakes and data warehouses.

It took about 8 hours a week to extract and load the company?s data for Stitch. Liv Up benefited from the ability to build their transformation phase to easily leverage the BI tools that were integral to their company.

ELT is a better approach when the destination is a cloud-native data warehouse like Google BigQuery, Amazon, Snowflake, Microsoft Azure SQL Data Warehouse, and Redshift. That?s because these organizations can transform their raw data at any time, when and as required for their use case, and not as a step in the data pipeline.

Amazon Redshift
The approach of Amazon Redshift is a PaaS (Platform-as-a-Service) that is highly scalable. It has the provision the clusters of nodes to customers as their computing and storage needs evolve. Each node has an individual CPU, RAM, and storage space. To set up Redshift, you must provision the clusters through AWS (Amazon Web Services). Redshift lets its users automatically add clusters in times of high demand.

Google BigQuery
The best thing about the architecture of BigQuery is that you don?t need to know anything about it. It is server less, that?s why its underlying architecture is hidden from the users (in a good way). It can scale to thousands of machines by structuring computations as an execution tree. BigQuery sends queries through a root server, intermediate servers, and finally leaf servers with local storage.

Microsoft Azure SQL Database
Azure SQL Data Warehouse is an elastic, large-scale DWaaS that leverages the broad ecosystem of SQL Server. It uses a distributed MPP (massively parallel processing) architecture that is designed to handle multiple operations simultaneously by several processing units that work independently and have their own dedicated memory and operating system. It collects data from databases and SaaS platforms into one powerful, fully-managed centralized repository. The compute and storage as billed separately, so they can scale independently.

Snowflake
Snowflake is a DWaaS (Data Warehouse-as-a-Service) that operates across multiple clouds including AWS, Microsoft Azure, and soon, Google Cloud. It separates the storage, computes, and services into detached layers, allowing them to scale independently. These automatically managed storage layers can hold structured or semi-structured data. The compute layer contains clusters, each of which can access all data but work independently and concurrently to enable automatic scaling, distribution, and rebalancing.

ELT Advantages for Businesses

The explosion in the types and volume of data to be processed by businesses can put a strain on the traditional data warehouses. Using an ETL process to manage millions of records in new formats can be quite expensive and time-consuming. This is where ELT offers numerous advantages over ETL:

Faster Time to Value
Generally, ELT provides a faster time to value which means business intelligence is available far more quickly. Whereas, ETL needs a time-intensive and resource-heavy transformation step before loading or integrating data.

Scalability
ELT tools are used along with the cloud data warehouses which are designed to auto-scale in the case of increased processing loads. The cloud platforms allow for almost unlimited scale that too within seconds or minutes, while the older generations of on-premises data warehouses need the organizations to order, install, and configure new hardware.

Flexibility
You can replicate the raw data into your data lake or data warehouse and transform it when and however you need them with the ELT process as it is adaptable and flexible. This makes it suitable for a wide variety of businesses, goals, and applications.

Simplifies Management
It separates the loading and transformation tasks, lowers the risk, minimizes the interdependencies between the processes, and streamlines the project management.

Leverages the Latest Technologies
It harnesses the power of new technologies for pushing improvements, compliance, and security across the enterprise. It also leverages the native capabilities of the modern big data processing frameworks and cloud data warehouses.

Future-proofs Data Sets
ELT implementations can be directly used for data warehousing systems. However, most of the time it is used in the data lake approach where data is collected from a range of sources, combined with the separation of the transformation process to make it easier for making future changes to the warehouse structure.

Lowers The Cost
Cloud-based ELT can result in a lower total cost of ownership, as an upfront investment in hardware is often unnecessary.

Though ELT is still evolving, it offers the promise of unlimited access to data, less development time, and significant cost savings, thus, it redefines data integration.

How ELT Works?

It is becoming increasingly common for data extraction from its source locations to get them loaded into a target data warehouse for transforming them into actionable business intelligence. This process is called ELT and involves the following steps:

Extract
This step is the same in both ETL and ELT data management approaches. The raw streams of data from virtual infrastructure, software, and applications are consumed in either their entireness or as per predefined rules.

Load
This is where the ELT differs from its earlier cousin ETL. Instead of delivering the raw data and loading it to a temporary processing server for transformation, ELT delivers the data directly to the destination storage. This helps to shorten the cycle between the extraction and delivery of the data.

Transform
The data warehouse sorts and normalizes the data and keeps part or all of it accessible for customized reporting. The overhead for storing a huge amount of data is higher, but it offers more opportunities for data mining for relevant BI in near real-time.

Why is ELT Better?

Let us take a closer look at the difference between ETL and ELT processes. The primary difference between ETL and ELT is the amount of data retained in the warehouses and where the data is transformed.

In ETL, the transformation of data is done before loading into a warehouse, which enables the analysts and business users to get the data they need faster. Moreover, they don?t have to build complex transformations or persistent tables in their business intelligence tools. Whereas, in ELT, the data is loaded into the warehouse or data lake as it is, without transformation before loading. This makes it an easier job for configuration as it only needs a source and a destination.

The ETL and ELT approaches for data integration differs in the following ways:

Load Time
It takes significantly longer to get data from the source system to the target system with ETL, whereas it is faster with ELT.

Transformation Time
ELT performs data transformation on-demand, using the computing power of the target system, which significantly reduces the wait times for transformation as with ETL.

Data Warehouse Support
ETL is a better approach for legacy on-premise data warehouses and structured data, while ELT is designed for the scalability of the cloud.

Complexity
Typically, the ETL tools have an easy-to-use GUI (Graphic User Interface) that simplifies the process. While ELT needs in-depth knowledge of BI tools, masses of raw data, and a database that can transform the data effectively.

Maintenance
ETL requires significant maintenance for updating data in the warehouse, whereas, with ELT, data is always available in near real-time.

Conclusion

Both the ELT and ETL processes have a place in today?s competitive scenario. Understanding the unique needs and strategies of a business is the key to determine which process will deliver the best outcomes. Businesses need a data warehouse to analyze data over time and deliver actionable BI. So, should you deploy your data warehouse on-premises at your own data center or in the cloud? The answer depends on factors like cost, scalability, control, resources, and security.

Some businesses may deploy a data warehouse on-premises, in the cloud, or a hybrid solution that combines both. An organization choosing an on-premises data warehouse must purchase, deploy, and maintain all the hardware and software.

However, as a cloud data warehouse is a SaaS, having no physical hardware, a business will pay for the storage space and the computing power they need at a given time. A business pays for the storage space and the computing power they need at a given time. Scalability is simply adding more cloud resources, and there?s no need to employ people or maintain the system as those tasks are handled by the provider. That?s why cloud-based data warehouses and ELT go hand-in-hand with regards to performance, scalability, and lower costs as compared to the on-premise databases and ETL.