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. For more ETL Testing Trianing
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. Learn more from ETL Online Course
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. Learn more skills from ETL Testing Certification
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.
Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage.
Just think about Amazon’s recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions. It’s a marketing machine, and its big data analytics capabilities have made it extremely successful.
The ability to analyze big data provides unique opportunities for your organization as well. You’ll be able to expand the kind of analysis you can do. Instead of being limited to sampling large data sets, you can now use much more detailed and complete data to do your analysis. However, analyzing big data can also be challenging. Changing algorithms and technology, even for basic data analysis, often has to be addressed with big data.
The first question that you need to ask yourself before you dive into big data analysis is what problem are you trying to solve? You may not even be sure of what you are looking for.
You know you have lots of data that you think you can get valuable insight from. And certainly, patterns can emerge from that data before you understand why they are there.
If you think about it though, you’re sure to have an idea of what you’re interested in.
For instance, are you interested in predicting customer behavior to prevent churn? Do you want to analyze the driving patterns of your customers for insurance premium purposes?
Are you interested in looking at your system log data to ultimately predict when problems might occur? The kind of high-level problem is going to drive the analytics you decide to use.
Alternately, if you’re not exactly sure of the business problem you’re trying to solve, maybe you need to look at areas in your business that need improvement. Even an analytics-driven strategy — targeted at the right area — can provide useful results with big data.
When it comes to analytics, you might consider a range of possible kinds, which are briefly outlined in the table.
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