{"id":32768,"date":"2023-07-04T03:10:00","date_gmt":"2023-07-04T10:10:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=32768"},"modified":"2023-06-29T14:58:52","modified_gmt":"2023-06-29T21:58:52","slug":"why-do-we-prefer-elt-rather-than-etl-in-the-data-lake-what-is-the-difference-between-etl-elt","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/07\/04\/why-do-we-prefer-elt-rather-than-etl-in-the-data-lake-what-is-the-difference-between-etl-elt\/","title":{"rendered":"Why Do We Prefer ELT Rather than ETL in the Data Lake? What is the Difference between ETL & ELT"},"content":{"rendered":"\n

INTRODUCTION TO ETL, ELT, AND DATA LAKE<\/strong><\/p>\n\n\n\n

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two different types of processes to move data from a source system to a destination system. ETL extracts raw data from a source and transforms it into a structured format and then loads it into a destination system. Transformation takes place on a secondary processing server before loading data into a destination system. However, ELT extracts data from the source and loads it directly into a destination system. Transformation takes place in a destination system or a database.<\/p>\n\n\n\n

ETL is in practice for over 20 years and is best for small datasets that require complex transformations. It also maintains the privacy and security of data. ELT is newer than ETL and ideal for large datasets that require high speed and efficiency. ELT is compatible with data lakes due to its ability to handle large and unstructured datasets. Selecting the appropriate method depends on factors, such as data volume, speed, privacy concerns, and maintenance costs.<\/p>\n\n\n\n

A data lake stores large amounts of structured, semi-structured, and unstructured data. Unlike data warehouses, data lakes\u2019 unrefined data allows data scientists to access all data in its original, raw, and untransformed state. Data lakes\u2019 high scalability and cost-effectiveness on multiple data formats make them an attractive option to store and analyse large amounts of data. With the ability to centralize, consolidate, and catalogue data, data lakes can help to eliminate data silos and to achieve better collaboration and integration of diverse data sources.<\/p>\n\n\n\n

UNDERSTANDING THE TRADITIONAL ETL PROCESS<\/strong><\/p>\n\n\n\n

Traditional ETL processes required IT staff, on-premise databases, and lengthy batch processing sessions, which sacrifice data quality as volumes grew. These methods were less suitable for unstructured data which requires interaction from data engineers and developers for each new data source. Moreover, the hardware required for an on-premise data warehouse was costly and difficult to scale and maintain. The increasing volume and variety of data sources made cloud data warehousing a preferred solution whereas traditional ETL processes delayed reporting and analytics. Traditional ETL without cloud-based ETL puts businesses at risk of missed opportunities and lost revenues.<\/p>\n\n\n\n

UNDERSTANDING THE MODERN ETL PROCESS<\/strong><\/p>\n\n\n\n

Modern ETL has numerous advantages over traditional ETL. With cloud-based ETL and rapid batch data processing, businesses can scale data operations with enhanced security features. SaaS (Software as a service) allows for backup, encryption, security, and infrastructure issues while moving data to the cloud.<\/p>\n\n\n\n

Cloud-deployed ETL products provide speed, scale, savings, and simplicity while maintaining security, governance, and compliance. Modern ETL tools also import and export structured and unstructured data from various sources and can easily integrate on-premises and cloud data warehouses.<\/p>\n\n\n\n

Real-time data pipelines ensure business decision-makers have constant and unlimited access to all the data of all the time. Companies can choose to transform data either before or after loading it into a data warehouse. This flexibility enables us to adapt data pipelines to specific needs and achieve high performance, especially for modern data scenarios such as business intelligence, artificial intelligence, and machine learning.<\/p>\n\n\n\n

DIFFERENCES BETWEEN ETL AND ELT<\/strong><\/p>\n\n\n\n