ETL vs ELT In Snowflake: What’s the Difference

<p>ETL vs. ELT In Snowflake: What&#8217;s the Difference? The rate at which today&#8217;s organizations collect data is unprecedented. According to Statista, the amount of data created, shared, and stored is projected to reach 180 zettabytes by 2025. Data availability is widespread, and the capacity to capture, format, and evaluate it promptly and accurately is a crucial driver for company success. Data-driven decision-making directs companies, and contextual data exposes new trends and patterns in the market that businesses can use to drive innovation. But just as quickly as the sheer volume of data has expanded, so has the complexity of data…</p>

<div data-elementor-type="wp-post" data-elementor-id="12661" class="elementor elementor-12661" data-elementor-post-type="blog"> <section class="elementor-section elementor-top-section elementor-element elementor-element-11329fe elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="11329fe" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-88bdddc" data-id="88bdddc" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-bc4235e elementor-widget elementor-widget-spacer" data-id="bc4235e" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-70d71a7 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="70d71a7" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ba692da" data-id="ba692da" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-a441aa5 elementor-widget elementor-widget-heading" data-id="a441aa5" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h1 class="elementor-heading-title elementor-size-default">ETL vs. ELT In Snowflake: What's the Difference?</h1> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-f4f9ea1 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="f4f9ea1" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c20899d" data-id="c20899d" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-a70e177 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="a70e177" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-96468fd elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="96468fd" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b4d8fec" data-id="b4d8fec" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-ce328bc elementor-widget elementor-widget-text-editor" data-id="ce328bc" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">The rate at which today&#8217;s organizations collect data is unprecedented. </span><a href="https://www.statista.com/statistics/871513/worldwide-data-created/#:~:text=Over%20the%20next%20five%20years%20up%20to%202025%2C%20global%20data%20creation%20is%20projected%20to%20grow%20to%20more%20than%20180%20zettabytes" target="_blank" rel="noopener"><span style="font-weight: 400;">According to Statista</span></a><span style="font-weight: 400;">, the amount of data created, shared, and stored is projected to reach 180 </span><a href="https://en.wikipedia.org/wiki/Zettabyte_Era#The_zettabyte" target="_blank" rel="noopener"><span style="font-weight: 400;">zettabytes</span></a><span style="font-weight: 400;"> by 2025.</span></p><p><span style="font-weight: 400;">Data availability is widespread, and the capacity to capture, format, and evaluate it promptly and accurately is a crucial driver for company success. Data-driven decision-making directs companies, and contextual data exposes new trends and patterns in the market that businesses can use to drive innovation.</span></p><p><span style="font-weight: 400;">But just as quickly as the sheer volume of data has expanded, so has the complexity of data itself.</span></p><p><span style="font-weight: 400;">Today&#8217;s data comes from more sources and formats than ever. As a result, structured data (e.g., names, dates, addresses), semi-structured data (e.g., email, HTML, JSON documents), and unstructured data (e.g., log files, videos, social media posts) must be managed and utilized to make better decisions faster.</span></p><p><span style="font-weight: 400;">Internal data sets like CRM or ERP systems are essential, but they’re not the only data sets that matter. Organizations must also account for social media data, Internet of Things (IoT) sensor data, clickstream data, and more. This volume and variety of data have given rise to a new set of challenges—and opportunities—for businesses across the globe.</span></p><p><span style="font-weight: 400;">Organizations are turning to the cloud to handle increased data volume, velocity, and variety. Cloud data warehouses (CDWs) like Snowflake provide the flexibility and scalability necessary to support a modern data strategy.</span></p><p><span style="font-weight: 400;">And to bring all of their disparate information to a single source of truth, organizations rely primarily on two approaches: extract, transform, load (ETL) and extract, load, transform (ELT).</span></p><p><span style="font-weight: 400;">But before we dive into ETL vs. ELT in Snowflake, let’s clearly define each process.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-4cc7ae0 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="4cc7ae0" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6abb954" data-id="6abb954" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-c3ea38f elementor-widget elementor-widget-heading" data-id="c3ea38f" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">What Is Extract, Transform, Load (ETL)? </h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-e4e5e45 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="e4e5e45" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-497c000" data-id="497c000" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-fc9a8dc elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="fc9a8dc" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-70a162b elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="70a162b" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a40feca" data-id="a40feca" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-aeba621 elementor-widget elementor-widget-text-editor" data-id="aeba621" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Extract, transform, load (ETL) is a process in data warehousing in which raw data is extracted from outside sources, transformed to fit operational needs (often using staging, cleansing, de-duping and merging techniques), then loaded into the end target database. ETL tools like Alteryx and Informatica offer a visual interface to help users design and automate the entire process.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-8fafb22 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="8fafb22" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-babf0b7" data-id="babf0b7" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-2482168 elementor-widget elementor-widget-image" data-id="2482168" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img loading="lazy" decoding="async" width="636" height="303" src="https://public-cdn.dlh.io/migrated/uploads/2022/09/Screen-Shot-2022-09-12-at-6.14.53-PM.png" class="attachment-large size-large wp-image-12666" alt="extract" srcset="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.14.53-PM.png 636w, https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.14.53-PM-300x143.png 300w" sizes="(max-width: 636px) 100vw, 636px" /> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-fc5c578 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="fc5c578" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b15749d" data-id="b15749d" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-c221151 elementor-widget elementor-widget-spacer" data-id="c221151" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-b32d284 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="b32d284" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3af576d" data-id="3af576d" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-5d1e8db elementor-widget elementor-widget-text-editor" data-id="5d1e8db" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">The ETL process typically follows this sequence:</span></p><ol><li style="font-weight: 400;" aria-level="1"><b>Extract data from homogeneous or heterogeneous data sources.</b><span style="font-weight: 400;"> This data can come from relational databases, flat files, XML, JSON, or Web services (e.g., SOAP or REST API).</span></li><li style="font-weight: 400;" aria-level="1"><b>Transform the data.</b><span style="font-weight: 400;"> Data transformation includes selecting, filtering, sorting, aggregating, joins, normalization (for relational databases), and denormalization (for flat files).</span></li><li style="font-weight: 400;" aria-level="1"><b>Load the transformed data into a destination database. </b><span style="font-weight: 400;">This is usually a data warehouse or data mart. The loading process can include upserts (update or insert) and inserts.</span></li></ol><p><span style="font-weight: 400;">Often, these three steps are performed in parallel because they can be quite resource-intensive.</span></p><p><span style="font-weight: 400;">Since data integration with ETL is a linear process, it is best suited for data transformations that are simple and well-defined such as relational data from onsite data warehouses. For larger or more complex data sets, ETL can be slow and cumbersome.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-7381fc6 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="7381fc6" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-11711ec" data-id="11711ec" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-63dd058 elementor-widget elementor-widget-spacer" data-id="63dd058" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-2e7be1c elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="2e7be1c" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a75ca8e" data-id="a75ca8e" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-7787ef0 elementor-widget elementor-widget-heading" data-id="7787ef0" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">What Is Extract, Load, Transform (ELT)?</h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-b384c8f elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="b384c8f" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0a6301f" data-id="0a6301f" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-a9be68f elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="a9be68f" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-c493528 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="c493528" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ca80b27" data-id="ca80b27" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-ad6d6a8 elementor-widget elementor-widget-text-editor" data-id="ad6d6a8" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Extract, load, transform (ELT) is a modern data integration method that leverages the power of the cloud to make it easier and faster to get value from data.</span></p><p><span style="font-weight: 400;">The target data store&#8217;s processing capabilities are used to transform data rather than a separate transformation engine. This simplifies the architecture by removing the transformation engine from the pipeline.</span></p><p><span style="font-weight: 400;">Another advantage of this technique is that scaling the target data store improves the ELT process. However, ELT performs best when the target system has sufficient processing capacity to transform information properly.</span></p><p><span style="font-weight: 400;">ELT tools offer pre-built connectors that make it easy to set up a pipeline.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-072a179 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="072a179" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cc4bcf1" data-id="cc4bcf1" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-7467133 elementor-widget elementor-widget-image" data-id="7467133" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img loading="lazy" decoding="async" width="643" height="309" src="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.27.50-PM.png" class="attachment-large size-large wp-image-12667" alt="example of pre-built pipeline" srcset="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.27.50-PM.png 643w, https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.27.50-PM-300x144.png 300w" sizes="(max-width: 643px) 100vw, 643px" /> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-986a72e elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="986a72e" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a17f2e2" data-id="a17f2e2" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-be0b095 elementor-widget elementor-widget-text-editor" data-id="be0b095" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">In general, ELT use cases are characterized by:</span></p><ul><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Data from multiple heterogeneous sources</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Complex transformations</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">The need for speed</span></li></ul><p> </p><p><span style="font-weight: 400;">A cloud-based data warehouse like Snowflake is the perfect platform for ELT because it is built for the cloud and offers a unique architecture that separates storage and computing. This separation allows data engineers to scale computing independently of storage, making it possible to quickly process large volumes of data. In addition, you only pay for the resources you use…gone are the days of capacity planning for the next 3-5 years.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-f4fd832 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="f4fd832" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-db16be4" data-id="db16be4" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-b727983 elementor-widget elementor-widget-heading" data-id="b727983" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">So, What's the Difference Between ETL and ELT?</h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-f6c4f15 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="f6c4f15" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-204a3ef" data-id="204a3ef" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-2692515 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="2692515" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-2d06b2f elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="2d06b2f" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3604b62" data-id="3604b62" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-c5c8b0b elementor-widget elementor-widget-text-editor" data-id="c5c8b0b" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">As their names imply, the main difference between ETL and ELT is when or where the transformation step occurs. The two key differences between the two processes are:</span></p><ul><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">In ETL, the transformation process happens before the data is loaded into the destination database. In ELT, data is first loaded into the destination database and then transformed.</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">In ETL, raw data is transformed into an optimized format for the destination database. In ELT, the information is extracted and loaded into the destination database in its raw form. The transformation step happens after the data is loaded.</span></li></ul><p><span style="font-weight: 400;">Let&#8217;s examine each process in detail.</span></p><h3><b>Data Extraction</b></h3><p><b>ETL</b><span style="font-weight: 400;">: Raw data is extracted from multiple heterogeneous data sources using API calls or SQL queries. The data is then saved in flat files or staging tables.</span></p><p><b>ELT</b><span style="font-weight: 400;">: Raw data is extracted from multiple heterogeneous data sources using API calls or SQL queries. The data is then loaded into the destination database.</span></p><h3><b>Data Transformation</b></h3><p><b>ETL</b><span style="font-weight: 400;">: Data transformation includes cleansing, de-duping, filtering, and aggregation. The data is then transformed into the format required by the destination database. During transformation, you can add and remove specific columns.</span></p><p><b>ELT</b><span style="font-weight: 400;">: The transformation step happens after the data is loaded into the destination database. Data transformation includes cleansing, de-duping, filtering, and aggregation. The data is then transformed into the format required by the destination database. Columns are directly added to the dataset with no modification support.</span></p><h3><b>Data Loading</b></h3><p><b>ETL</b><span style="font-weight: 400;">: The transformed data is then loaded into the destination database. This can be done using bulk loaders or incremental loaders.</span></p><p><b>ELT</b><span style="font-weight: 400;">: The raw data is loaded directly into the target system.</span></p><h3><b>Speed of Implementation</b></h3><p><b>ETL</b><span style="font-weight: 400;">: ETL can be slow to implement because it is a linear process. Each data set must go through the extract, transform, and load steps before reaching the target database for analysis.</span></p><p><b>ELT</b><span style="font-weight: 400;">: ELT is a faster process because it leverages the processing power of the target system. The data is loaded into the target first, then transformed in parallel.</span></p><h3><b>Code-Based Data Transformation</b></h3><p><b>ETL</b><span style="font-weight: 400;">: Data is replicated and transformed on an intermediary server prior to being loaded into the target system.</span></p><p><b>ELT</b><span style="font-weight: 400;">: Data is transformed after it has been replicated into the target system by taking advantage of the performance and scale of the data cloud. dbt is a market leading transformation tool used by data cloud organizations.</span></p><h3><b>Data Security and Privacy</b></h3><p><b>ETL</b><span style="font-weight: 400;">: Transforming data before it’s loaded into the target system can remove PII and obfuscate data values.</span></p><p><b>ELT</b><span style="font-weight: 400;">: Loading raw data allows special handling for sensitive data where data is tagged accordingly, allowing you to apply security and masking rules by role. </span></p><h3><b>Transformation Output</b></h3><p><b>ETL</b><span style="font-weight: 400;">: ETL only loads data that is required for analytics or reporting, leaving other non-essential data behind in the source systems. It is typically structured data.</span></p><p><b>ELT</b><span style="font-weight: 400;">: In the ELT process, all the extracted data is loaded at once. The output can be structured data, semi-structured data, or unstructured data.</span></p><h3><b>Data Lake Compatibility</b></h3><p><b>ETL</b><span style="font-weight: 400;">: Not compatible with cloud data lakes due to its transformation step.</span></p><p><b>ELT</b><span style="font-weight: 400;">: Compatible with cloud data lakes.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-783a3bd elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="783a3bd" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9408621" data-id="9408621" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-48d0863 elementor-widget elementor-widget-heading" data-id="48d0863" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">ETL vs. ELT: Pros and Cons </h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-590e0fc elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="590e0fc" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1560639" data-id="1560639" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-ae70b8c elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="ae70b8c" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-566a731 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="566a731" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b997c0b" data-id="b997c0b" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-9c31f14 elementor-widget elementor-widget-text-editor" data-id="9c31f14" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <h3><b>The Benefits of ETL</b></h3> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-9687529 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="9687529" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bd2e483" data-id="bd2e483" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-a6b8683 elementor-widget elementor-widget-image" data-id="a6b8683" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img loading="lazy" decoding="async" width="621" height="322" src="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.32.13-PM.png" class="attachment-large size-large wp-image-12668" alt="the etl process" srcset="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.32.13-PM.png 621w, https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.32.13-PM-300x156.png 300w" sizes="(max-width: 621px) 100vw, 621px" /> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-5230fdb elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="5230fdb" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-842df79" data-id="842df79" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-fb92b34 elementor-widget elementor-widget-spacer" data-id="fb92b34" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-19bb952 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="19bb952" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f2e18ab" data-id="f2e18ab" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-279fa95 elementor-widget elementor-widget-text-editor" data-id="279fa95" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Although ETL is a process that has been around for decades, it is still the most popular data warehousing method. The main benefits of ETL are:</span></p><ul><li style="font-weight: 400;" aria-level="1"><b>Familiarity</b><span style="font-weight: 400;">: The ETL process is well-known and has been used for a long time. There are also a lot more ETL tools available on the market.</span></li><li style="font-weight: 400;" aria-level="1"><b>Support</b><span style="font-weight: 400;">: Many tools are available to support ETL, including open source and commercial products.</span></li><li style="font-weight: 400;" aria-level="1"><b>Faster Analysis</b><span style="font-weight: 400;">: Although ETL can be slow to implement, it is faster when it comes to data analysis. As shown in the graphic above, the transformation step happens before the data is loaded into the target data warehouse, so the data is in a format that can be easily queried and analyzed.</span></li><li style="font-weight: 400;" aria-level="1"><b>Compliance</b><span style="font-weight: 400;">: When organizations need to comply with regulations such as GDPR, ETL enables them to encrypt sensitive data prior to loading it into the target system, ensuring data security and compliance.</span></li></ul><h3><b>The Drawbacks of ETL</b></h3><p><span style="font-weight: 400;">Although familiar and predictable, ETL can have some drawbacks, including:</span></p><ul><li style="font-weight: 400;" aria-level="1"><b>Inflexibility</b><span style="font-weight: 400;">: Because the transformation step happens before the data is loaded into the database, ETL can be rigid. The entire process needs to be updated if the data format or structure changes.</span></li><li style="font-weight: 400;" aria-level="1"><b>Slow Loading and High Costs</b><span style="font-weight: 400;">: Because the ETL process runs on separate servers, ETL can be a time-consuming and expensive process to set up and maintain.</span></li><li style="font-weight: 400;" aria-level="1"><b>Lack of High-Volume Support</b><span style="font-weight: 400;">: ETL best suits smaller data sets with relevant, in-depth data. It can struggle with large data sets or data that changes frequently.</span></li><li style="font-weight: 400;" aria-level="1"><b>Maintenance Burden</b><span style="font-weight: 400;">: The ETL process can be complex, making it difficult to maintain. If something goes wrong, troubleshooting and fixing the issue can be difficult.</span></li></ul><h3><b>The Benefits of ELT</b></h3> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-497b699 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="497b699" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ec8e77e" data-id="ec8e77e" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-0a5cc55 elementor-widget elementor-widget-image" data-id="0a5cc55" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img loading="lazy" decoding="async" width="622" height="320" src="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.33.51-PM.png" class="attachment-large size-large wp-image-12669" alt="the elt process" srcset="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.33.51-PM.png 622w, https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.33.51-PM-300x154.png 300w" sizes="(max-width: 622px) 100vw, 622px" /> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-a536f6d elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="a536f6d" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ca3bf5f" data-id="ca3bf5f" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-19e1c28 elementor-widget elementor-widget-spacer" data-id="19e1c28" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-0bb155f elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="0bb155f" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cbb5fc2" data-id="cbb5fc2" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-fc95f97 elementor-widget elementor-widget-text-editor" data-id="fc95f97" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">ELT has become increasingly popular in recent years as organizations look for ways to speed up their data warehousing processes. Many of its benefits build off of the drawbacks of ETL:</span></p><ul><li style="font-weight: 400;" aria-level="1"><b>Flexibility and Support for Many Data Formats</b><span style="font-weight: 400;">: ELT can perform data ingestion in any format. Since the data lake accepts structured or unstructured data, there is no need to worry about schema changes.</span></li><li style="font-weight: 400;" aria-level="1"><b>Scalability</b><span style="font-weight: 400;">: ELT can scale to accommodate large data sets and high volumes of data, making it a scalable solution for data integration.</span></li><li style="font-weight: 400;" aria-level="1"><b>As-Needed Transformation for Greater Resource Efficiency</b><span style="font-weight: 400;">: ELT transforms data on-demand, only transforming it when needed. In the ETL process, data is converted before it is loaded into the target database, even if it is not needed for analytics or reporting.</span></li><li style="font-weight: 400;" aria-level="1"><b>High Availability</b><span style="font-weight: 400;">: All data is loaded into the data lake in the ELT process. This makes data available for transformation as soon as it is loaded, so tools that don&#8217;t have real-time data requirements can still use the data lake.</span></li><li style="font-weight: 400;" aria-level="1"><b>Implementation Speed</b><span style="font-weight: 400;">: ELT can be faster to implement than ETL, giving data teams more time to query and analyze data. As shown in the above graphic, the source data is loaded and transformed simultaneously, cutting out an entire step of the process.</span></li></ul><h3><b>The Drawbacks of ELT</b></h3><p><span style="font-weight: 400;">Although ELT has many benefits, it also has some drawbacks:</span></p><ul><li style="font-weight: 400;" aria-level="1"><b>Loading Data Before Transformation Results In Compliance Issues</b><span style="font-weight: 400;">: One of the main benefits of ETL is its ability to encrypt sensitive data before it is loaded into the target system. With ELT, data is loaded into the data lake before it is transformed, meaning sensitive data requires greater protection initiatives.</span></li><li style="font-weight: 400;" aria-level="1"><b>Lack of Technological and Support Maturity</b><span style="font-weight: 400;">: Although the number of tools that support ELT is growing as the technology becomes more commonplace, it is still not as widespread as ETL. This can make finding support and resources more difficult.</span></li><li style="font-weight: 400;" aria-level="1"><b>Slow Analysis Speed</b><span style="font-weight: 400;">: Since the need for analysis precedes the transformation step in ELT, data might not be available in the format required for analysis. This can make querying and analyzing data more difficult and time-consuming.</span></li></ul> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-11c70d3 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="11c70d3" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ec4da2b" data-id="ec4da2b" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-0e66a72 elementor-widget elementor-widget-heading" data-id="0e66a72" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Use Cases for ETL Processes</h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-22fb861 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="22fb861" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2ee435f" data-id="2ee435f" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-b2a6d75 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="b2a6d75" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-b418111 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="b418111" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0412127" data-id="0412127" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-f7d2bf8 elementor-widget elementor-widget-text-editor" data-id="f7d2bf8" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">ELT offers a modern alternative to ETL, but there are still a few everyday use cases for ETL.</span></p><h3><b>1. Data Integration From Multiple Data Sources</b></h3><p><span style="font-weight: 400;">Frequently, company data is siloed in different departments, applications, or—in the event of an acquisition—different companies altogether. This data needs to be integrated into a central location to get a complete view of the company. ETL can help with this by extracting data from multiple sources, transforming it into a consistent format, and loading it into a central data warehouse.</span></p><p><span style="font-weight: 400;">For example, if two businesses combine their operations, they will have many suppliers, business partners, and customers in common. But since each party likely uses a different data repository, data formats, and schemas, this data will need to be transformed before data teams can load it into the new company&#8217;s central data warehouse.</span></p><h3><b>2. Migrating From Legacy Systems to Snowflake</b></h3><p><span style="font-weight: 400;">As data architectures have evolved, many companies are moving away from traditional data warehouses to cloud-based data warehouses that support ELT. If a company migrates its data to Snowflake, it might use an ETL process to extract its data from the old system and load it into the new one.</span></p><p><span style="font-weight: 400;">An ETL process would be particularly helpful in this case if the data needs to be cleansed or transformed before it is loaded into the new data warehouse. For example, if the legacy system uses a different date format than the new system, the data will need to be transformed before it is loaded.</span></p><p><span style="font-weight: 400;">Similarly, organizations can use an ETL process to migrate data from on-premises systems to cloud-based storage. This can be helpful if a company wants to take advantage of the scalability and flexibility of the cloud without having to rework its data architecture completely.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-dbee368 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="dbee368" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-84264d7" data-id="84264d7" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-92d1d11 elementor-widget elementor-widget-heading" data-id="92d1d11" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Use Cases for ELT Processes </h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-68e3d47 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="68e3d47" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3e47260" data-id="3e47260" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-13a52b8 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="13a52b8" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-0b0039b elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="0b0039b" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-004e87d" data-id="004e87d" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-32175f5 elementor-widget elementor-widget-text-editor" data-id="32175f5" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">There are many use cases for the extract, load, transform process, but some of the most common are:</span></p><h3><b>1. Managing Large Amounts of Enterprise Data</b></h3><p><span style="font-weight: 400;">As companies grow, they often accumulate large amounts of data from different departments and applications. With so many data sources, it can be difficult to manage without a central system.</span></p><p><span style="font-weight: 400;">With ELT, the data pipelines don&#8217;t need to do much heavy lifting—instead, they can focus on loading the data into the data lake quickly and efficiently. Then, the transformation step can be done later, when it is more convenient.</span></p><p><span style="font-weight: 400;">This approach to data integration can be especially helpful for companies that are dealing with a high volume of data from many different sources. For example, a company might have customer data from its website, CRM, and ERP system. Rather than trying to transform all of this data in real-time, it can be loaded into the data lake and transformed later.</span></p><p><span style="font-weight: 400;">This approach can also be helpful for companies that want to take advantage of new technologies like AI and machine learning. With ELT, the data can be loaded into the data lake and then transformed into the format required for these technologies.</span></p><h3><b>2. Big Data Analytics</b></h3><p><span style="font-weight: 400;">ELT is also well-suited for big data analytics. This is because the process of loading and transforming data can happen in parallel, speeding up the data transformation process.</span></p><p><span style="font-weight: 400;">Advanced, lightning-fast analytics enable companies to:</span></p><ul><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Improve customer service by identifying and resolving issues quickly</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Make improvements to your website&#8217;s user experience based on user behavior data</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Detect fraud and other security threats in real-time</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Generate new revenue streams by identifying new market trends</span></li><li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Sustain a competitive advantage by being the first to market with new products and services</span></li></ul> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-c7b8995 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="c7b8995" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-99cfc77" data-id="99cfc77" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-3411802 elementor-widget elementor-widget-heading" data-id="3411802" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">ETL vs. ELT: Which Is Better for Snowflake? </h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-f6627e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="f6627e3" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d89aa97" data-id="d89aa97" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-a4f2401 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="a4f2401" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-2ef98e6 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="2ef98e6" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5c786d4" data-id="5c786d4" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-551baba elementor-widget elementor-widget-text-editor" data-id="551baba" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Snowflake is a SaaS data warehouse that is built for the cloud. It provides analytic data storage for cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-08d5120 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="08d5120" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-642ac5f" data-id="642ac5f" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-b15ab11 elementor-widget elementor-widget-image" data-id="b15ab11" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img loading="lazy" decoding="async" width="641" height="398" src="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.38.22-PM.png" class="attachment-large size-large wp-image-12670" alt="modern data landscape" srcset="https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.38.22-PM.png 641w, https://www.datalakehouse.io/wp-content/uploads/2022/09/Screen-Shot-2022-09-12-at-6.38.22-PM-300x186.png 300w" sizes="(max-width: 641px) 100vw, 641px" /> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-7d01f80 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="7d01f80" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-113a38f" data-id="113a38f" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-c0789ee elementor-widget elementor-widget-text-editor" data-id="c0789ee" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">As shown above, Snowflake&#8217;s architecture and security features enable integration with third-party tools, ELT/ETL, business intelligence systems, and AI/ML workflows.</span></p><p><span style="font-weight: 400;">Since Snowflake is a cloud-based data warehouse, it is well-suited for ELT processes. In fact, many of Snowflake&#8217;s features are designed specifically for ELT.</span></p><p><span style="font-weight: 400;">For example, Snowflake&#8217;s micro-partitioning feature automatically partitions data into small, equal-sized pieces. This makes it easier to load and transform data in parallel, which makes the process much faster.</span></p><p><span style="font-weight: 400;">In addition, Snowflake&#8217;s &#8220;zero-copy clones&#8221; feature enables users to create copies of data without actually copying the data. This allows users to experiment with different transformations without worrying about the impact on performance.</span></p><p><span style="font-weight: 400;">Snowflake&#8217;s &#8220;time travel&#8221; feature enables users to view data as if it existed at any time. This can be helpful for troubleshooting issues or auditing data.</span></p><p><span style="font-weight: 400;">So, which is better for Snowflake: ETL or ELT? The answer really depends on the use case. However, many users find that ELT is a better fit for Snowflake because of its cloud infrastructure.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-3ce149a elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="3ce149a" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-43c2cf0" data-id="43c2cf0" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-6a8486c elementor-widget elementor-widget-heading" data-id="6a8486c" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Streamline Data Transformations With DataLakeHouse</h2> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-b0707c1 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="b0707c1" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5b3048e" data-id="5b3048e" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-0806e39 elementor-widget-divider--view-line elementor-widget elementor-widget-divider" data-id="0806e39" data-element_type="widget" data-widget_type="divider.default"> <div class="elementor-widget-container"> <div class="elementor-divider"> <span class="elementor-divider-separator"> </span> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-36b18b1 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="36b18b1" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b2db945" data-id="b2db945" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-f4c5fcb elementor-widget elementor-widget-text-editor" data-id="f4c5fcb" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">DataLakeHouse is a 100% Snowflake-focused end-to-end analytics platform that integrates your most-used sources to Snowflake so you can focus on developing the applications that are important to your business. Simply load data from any source to your destination on the Snowflake Data Cloud and let our platform do the rest.</span></p><p><span style="font-weight: 400;">Our comprehensive suite of data tools and </span><a href="https://www.datalakehouse.io/integrations/"><span style="font-weight: 400;">integrations</span></a><span style="font-weight: 400;"> includes:</span></p><ul><li style="font-weight: 400;" aria-level="1"><b>ELT Data Ingestion</b><span style="font-weight: 400;">: DataLakeHouse can ingest data from dozens of popular sources—including databases, files, and streaming data—into Snowflake. We also provide pre-built connectors for popular sources like Salesforce, MySQL, Square, HubSpot, Shopify and NetSuite.</span></li><li style="font-weight: 400;" aria-level="1"><b>Industry-Specific Pre-Built Models</b><span style="font-weight: 400;">: DataLakeHouse comes with pre-built models for popular industries like eCommerce, healthcare, and financial services. These models can be used to accelerate the development of ELT processes.</span></li><li style="font-weight: 400;" aria-level="1"><b>AI/ML-Powered Transformation</b><span style="font-weight: 400;">: DataLakeHouse&#8217;s AI/ML-powered transformation engine enables users to quickly and easily transform data into the format required for their applications.</span></li></ul><p><a href="https://www.datalakehouse.io/book/demo/"><span style="font-weight: 400;">Request a demo today</span></a><span style="font-weight: 400;"> to learn more about how DataLakeHouse can help you develop complex ELT processes.</span></p> </div> </div> </div> </div> </div> </section> </div>