The Role of Traditional ETL in Big Data
ETL tools combine three important functions (extract, transform, load) required to get data from one big data environment and put it into another data environment. Traditionally, ETL has been used with batch processing in data warehouse environments. Data warehouses provide business users with a way to consolidate information to analyze and report on data relevant to their business focus. ETL tools are used to transform data into the format required by data warehouses.
The transformation is actually done in an intermediate location before the data is loaded into the data warehouse. Many software vendors, including IBM, Informatica, Pervasive, Talend, and Pentaho, provide ETL software tools.
ETL provides the underlying infrastructure for integration by performing three important functions:
Extract: Read data from the source database.
Transform: Convert the format of the extracted data so that it conforms to the requirements of the target database. Transformation is done by using rules or merging data with other data.
Load: Write data to the target database.
However, ETL is evolving to support integration across much more than traditional data warehouses. ETL can support integration across transactional systems, operational data stores, BI platforms, MDM hubs, the cloud, and Hadoop platforms. ETL software vendors are extending their solutions to provide big data extraction, transformation, and loading between Hadoop and traditional data management platforms.
ETL and software tools for other data integration processes like data cleansing, profiling, and auditing all work on different aspects of the data to ensure that the data will be deemed trustworthy. ETL tools integrate with data quality tools, and many incorporate tools for data cleansing, data mapping, and identifying data lineage. With ETL, you only extract the data you will need for the integration.
ETL tools are needed for the loading and conversion of structured and unstructured data into Hadoop. Advanced ETL tools can read and write multiple files in parallel from and to Hadoop to simplify how data is merged into a common transformation process. Some solutions incorporate libraries of prebuilt ETL transformations for both the transaction and interaction data that run on Hadoop or a traditional grid infrastructure.
Data transformation is the process of changing the format of data so that it can be used by different applications. This may mean a change from the format the data is stored in into the format needed by the application that will use the data. This process also includes mapping instructions so that applications are told how to get the data they need to process.
The process of data transformation is made far more complex because of the staggering growth in the amount of unstructured data. A business application such as a customer relationship management has specific requirements for how data should be stored. The data is likely to be structured in the organized rows and columns of a relational database. Data is semi-structured or unstructured if it does not follow rigid format requirements.
The information contained in an e-mail message is considered unstructured, for example. Some of a company’s most important information is in unstructured and semi-structured forms such as documents, e-mail messages, complex messaging formats, customer support interactions, transactions, and information coming from packaged applications like ERP and CRM.
Data transformation tools are not designed to work well with unstructured data. As a result, companies needing to incorporate unstructured information into its business process decision making have been faced with a significant amount of manual coding to accomplish the required data integration.
Given the growth and importance of unstructured data to decision making, ETL solutions from major vendors are beginning to offer standardized approaches to transforming unstructured data so that it can be more easily integrated with operational structured data.