ETL/ELT Development Services for Data-Driven Decisions
- These tools ensure that data sets are structured and standardized through a series of transformations before entering the repository, allowing for more efficient utilization of OLAP tools and SQL queries. However, as data volumes and types expand, the ETL process has become increasingly inefficient, expensive, and time-consuming. It is where ELT comes into play, offering a solution to these challenges.
- Due to the rapid growth of data sources and the increasing demand for processing large data sets for business intelligence and big data analytics purposes, ELT has gained popularity as an alternative to traditional data integration methods.ELT, which stands for extraction, loading, and transformation, reverses the order of the last two steps in the ETL process. After the data is extracted from databases, it is directly loaded into a central repository where all transformations occur without needing an intermediate database.
- Modern technology makes This approach possible, enabling the storage and processing of massive amounts of data in any format. Apache Hadoop, open-source software originally designed for continuous data retrieval from diverse sources, regardless of their type, has played a significant role in enabling this methodology. Cloud data warehouses like Snowflake, Redshift, and BigQuery also support ELT by leveraging shared storage and computation resources, leading to high scalability.
It's better to use ETL if...
- You need compliance with established standards for protecting sensitive customer data is crucial. Healthcare organizations, for instance, must comply with the HIPAA Security Rule. Therefore, they may opt for ETL to mask, encrypt, or delete vulnerable data before uploading it to the cloud.
- You only specialize in working with structured data and small pieces of information.
- In real-world scenarios, the extraction, transformation, and loading (ETL) process is commonly used when upgrading legacy systems, specifically relational databases, managed internally by your company. One example is extracting Electronic Health Record (EHR) data while upgrading a legacy EHR system. In this case, patient data must be carefully selected and extracted from the existing system and transformed into the appropriate format to integrate with the new system seamlessly.
- Real-time decision-making is crucial in your strategy. The advantage of ELT lies in its fast data integration, enabling the target system to convert and download data simultaneously. As a result, you can generate analytics that are close to real-time.
- Your company specializes in handling large volumes of structured and unstructured data. A prime example is working with a transportation company that utilizes telematics devices in its vehicles. This technology generates substantial data from sensors, video recorders, and GPS trackers. Processing such extensive data necessitates significant resources and investments. However, with ELT, you can save costs while achieving enhanced performance.
- Working with cloud projects or hybrid architectures often involves using modern ETL processes. While these processes have evolved to support cloud data stores, they typically require a separate engine to perform transformations before uploading the data to the cloud. However, ELT (Extract, Load, Transform) eliminates the need for intermediate processing engines, making it a more suitable choice for cloud and hybrid systems.
- ELT is specifically designed to achieve the primary objectives of Big Data, which include handling large volumes of data, managing diverse data types, processing data at high speeds, and ensuring data reliability.
- Your data science team requires access to all the "raw" data for machine learning projects.
- Your project aims to leverage the scalability of cloud storage and data lakes to support its growth.
Our architect begins the onboarding process by conducting a thorough system overview. Collaborating with your team, they assess your current tech stack, provide alternative options, and determine the ideal team composition.
Following that, our ETL/ELT engineers come into play. Whether or not you already have QA engineers, PM, or BA in your team, we can start the project with or without them. If additional support is required, we have a pool of over 250 talented professionals ready to assist you.
We ensure a seamless transition by preparing acceptance criteria and test cases. It guarantees that existing functionality remains intact as we introduce changes.
Prioritizing test cases allows us to verify critical business parameters effectively.
At this stage, active knowledge transfer takes place. Our team develops a deep understanding of your business, enabling them to see the bigger picture behind your ETL/ELT project.
We dive into system modernization and optimization with a comprehensive understanding of your system. Our engineers strictly adhere to your specifications and can suggest system improvements if desired.
- 01The first step in both processes The process always begins by extracting and duplicating data from various sources such as ERP and CRM systems, SQL and NoSQL databases, SaaS applications, web pages, unstructured files, emails, mobile applications, etc. Due to the intricacies of each source system, the initial phase can be quite intricate. Data is typically extracted in one of three ways. • Full extraction is necessary for systems that cannot differentiate between new or changed records. In these cases, extracting all records, including old and new ones, is the only way to retrieve data from the system. • Partial extraction with update notifications is more convenient for extracting data from source systems. This approach is possible if the systems send notifications about record changes. With this method, there is no need to download all the data, as only the relevant updates can be retrieved. • Incremental retrieval, or partial retrieval without update notifications, allows retrieving only the changed records. This method is useful when update notifications are unavailable but still provides an efficient way to extract the necessary data. Users must plan when using the ETL method to determine which data elements should be extracted for subsequent conversion and loading. In contrast, ELT enables the rapid extraction of all data. Users have the flexibility to decide later which data to transform and analyze.
- 02The second step in ETL / third step in ELT The transformation phase involves a series of actions to prepare the data for change, either to align it with the requirements of another system or to achieve a specific outcome. Transformations may include the following actions: • Sort and filter data to remove irrelevant items • Remove duplicates and clean up the data • Translate and convert data as needed • Delete or encrypt sensitive information for protection • Merge or split tables as required, and more. These operations occur outside the target system's preparation phase in the ETL process. Data engineers are responsible for implementing these processes. For instance, in OLAP data warehouses, only relational data structures can be stored, so data needs to be converted to SQL-readable format first. Once the conversions are done, they cannot be changed, making ETL inflexible. If you want to apply a new type of analysis to the already converted data, you may have to rebuild the entire data pipeline. On the other hand, the ELT method provides flexibility and convenience for transformations. Data is directly transferred to the data warehouse, data lake, or data lakehouse, where it can be validated, structured, and transformed in various ways and at any time. Additionally, since "raw" data is stored indefinitely, it can undergo countless transformations. Data analysts can assist data engineers in performing SQL transformations because everything happens within the target system.
- 03The second step in ELT/third step in ETL. This step involves transferring the data to the target storage system for user access. The ETL process flow imports prepared data from an intermediate database into the target data store or database. It can be done through SQL commands for individual records or by batch loading with a script. On the other hand, ELT sends the raw data directly to the target storage location without using an intermediate layer. It saves time in the extraction-delivery cycle. The data can be loaded completely or partially. The ELT method is flexible and convenient for transformations because the data is transferred directly to the data warehouse, data lake, or data lakehouse. It can be validated, structured, and transformed in various ways. Additionally, the "raw" data can undergo numerous transformations as it is stored indefinitely. Data analysts can assist data engineers in performing transformations using SQL within the target system.