Examples

Data integration plays a vital role in many solutions that otherwise would have to rely on cumbersome code development. This often lacks transparency and agility for both technical and business users. The following list touches some basic data integration use cases we’ve seen our customers succeed in by deploying CloverETL.

Automate delivery of data to clients based on their individual needs

Automate delivery of data to clients based on their individual needs

Imagine being a data management department responsible for processing and delivering data to hundreds or even thousands of clients; each with specific requirements on format, form of delivery and schedule. Mapping data and meeting quality standards quickly becomes a complexity so serious that the project does not proceed and the problem remains. A data integration framework can help your developers focus on data issues rather than low level implementations.

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Consolidate data from cloud applications

Consolidate data from cloud applications

As cloud services have become core systems for many organizations, getting data in and out and sharing data across cloud and on-premise platforms is now a fundamental discipline of data integration. CloverETL features an extensive and customizable set of connectors designed specifically for cloud use. Be it a data warehousing application running in Amazon Redshift or cloud storage facilitated by HDFS, MongoDB, CouchDB, HP Vertica or applications like SalesForce, Google API, Twitter or NetSuite, CloverETL connects these systems via Web Services REST and SOAP APIs with ease.

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For Porch, an online home improvement marketplace, running their content management system requires a lot of upfront data integration. Porch selected CloverETL to handle the job, using it for core integration as well as analytical workloads.

Eric Schleicher, Porch.com, CTO   →  Watch Video

Extract reporting data from CRM & Accounting

Extract reporting data from CRM & Accounting

Getting data for reporting is not just about being able to connect to data sources and visualizing them. Great business intelligence and business discovery solutions require data to be ‘curated’ and transformed before presenting it. Many businesses are aware of this need but accept this as a pain and commit to a routine of unnecessary manual labor - from exports that are hard to automate using limited tools to going through data sets and fixing it in Excel. Much of these tasks can be automated so that you can spend your time on smart analysis of your data, not basic data massaging.

Replace legacy Warehouse Builder with a more flexible tool

Replace legacy Warehouse Builder with a more flexible tool

Data warehousing has been the traditional discipline for ETL tools for decades now. It has been dominated by relational databases like Oracle or Microsoft SQL Server. Today, traditional stringent data warehouses are being replaced with more agile approaches, such as with data marts and data lakes. However, there is still value in building a DWH; the success of Amazon Redshift is a great example. Today’s warehouse will combine a wide variety of data sources, online and offline, from traditional relational databases, through omnipresent Excel spreadsheets up to cloud and big data. The ETL process has to be able to respond to these requirements, CloverETL guarantees that this happens.

Replace scripts with visual logic

Replace scripts with visual logic

DBAs often resort to what they know best: coding and scripting using their favorite programming languages, and do everything by hand. This approach work fine for small projects. However, as data grows in volume, formats, protocols and the project gets increasingly complicated with scheduling, external dependencies, team sharing and transparency need, it can slow you down drastically. A visual ETL tool brings an easy to understand, collaborative, standardized environment where developers, management and even clients can easily see what happens to their data.

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Send data from DBs and files into AWS Redshift data lake

Send data from DBs and files into AWS Redshift data lake

An extremely common use case for data integration - bring all data that’s available from a wide variety of data sources ranging from all sorts of RDBMS (Oracle, MS SQL, Postgres, MySQL, Informix, DB2), text and CSV files and, of course, Excel spreadsheets, into a single data lake in Amazon Redshift. CloverETL offers connectors to all standard data sources and can be extended to support proprietary formats. The important part of the story is ‘orchestration’ - which ensures that loads happen in a transparent workflow, with effective error handling and permitting the chance of simple recovery.

Pull aggregated data from Hadoop into a DWH near real-time

Pull aggregated data from Hadoop into a DWH near real-time

Hadoop is a great tool that provides invaluable insights into big data. However, the results need to live somewhere and the process needs to be automated in an enterprise environment. CloverETL is often used to serve as this meeting point between modern data processing techniques and time-proven core systems. We’ve seen many scenarios where big data processing is a significant added value to otherwise more traditional data architectures - like a data warehouse, data lake or other forms of storing valuable insights.

In order to combine their data sources into Tableau quickly and with minimal labor, it was necessary for the small digital insights team within EE to choose a data integration platform that could handle their data rapidly and offered stellar support.

Dwayne Browne, EE, Digital Insights Manager   →  Watch Video

Transform financial transaction data into a unified format

Transform financial transaction data into a unified format

Unification of data formats to help new systems understand legacy software and vice versa is another key of data integration. Usually a great deal of work for data migration and systems integration projects, it can get complicated in a quickly changing environment. CloverETL can remove this complexity because it will read any file, text or binary, convert character encoding, parse through non-homogenous (header-body-footer) structure, read XML and text based formats like HL7 or EDI.

We need something for our junior team members to easily manage. Our current Python scripts are getting too complicated.

Automate the processing of large numbers of Excel files

Automate the processing of large numbers of Excel files

Microsoft Excel spreadsheets are excellent for users and yet often present a nightmare scenario for those who need to access and manage all of the good data buried in them. CloverETL combines two key features that will help you to love Excel again. First, you can visually select regions of data, multiline records, horizontal or vertical, all can be read or written in CloverETL. Second, CloverETL is a powerful automation tool with jobflows, file management, error recovery and monitoring. Clover will remove the pain of ‘Excel Anarchy’.

Validate data quality before analytics

Validate data quality before analytics

CloverETL offers a powerful set of data quality tools to help you get data accurate before using it for analytics. Curating data is a process that may seem to require a lot of attention, however it pays back in accurate and reliable BI results. CloverETL Validator makes this simple and is an important tool for defining shareable and centralized data quality rules. Remember, the insight provided by BI, analytics and discovery tools can only be as good as you are - and the data you’re using!

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Prepare geospatial data for analytics

Prepare geospatial data for analytics

Just like any other type of data – monetary values, times and dates, etc. – geographical data pose interesting challenges to an ETL developer. CloverETL can help, by being used to parse and transfer geographical information using its extensibility and code embedding capabilities.

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