ETL solutions are seldom only that — many are data management, BI and analytics tools. It makes selecting an ETL module that much trickier. Your stakeholders’ expectations are riding on your software search, and you don’t want to fall short when a business decision is on the line.

We’ve got you covered with a handy ETL requirements checklist, helpful tips and resources to start the procurement process.

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Modern ETL tools work closely with data warehouses to centralize digital assets, helping enterprises stay competitive by providing a single, unique source of truth. With advanced aggregation and preparation capabilities, they ingest all types of information, including unstructured information.

With functions, filters and transforms, ETL software ensures analysis-ready data for BI and analytics systems.

This article discusses the following key topics.

• Factors To Consider
• Key Requirements
• How To Choose The Right System
• Next Steps

Software selection starts with a needs assessment that asks, “why does our organization need ETL software?”

Factors To Consider

Evaluate your business needs internally by asking the following questions.

What Are Your Goals?

When seeking new software, it pays to be mindful of your teams’ and stakeholders’ expectations. Else it’s like shooting in the dark — you don’t know where you’re going and are likely to miss your target.

Here are some considerations when conducting a needs assessment.

• Do you want a standalone ETL module or a solution with end-to-end information management incorporating ETL processes? Will an integrated product suite fit?
• Is technical debt like legacy software an issue? Is cloud migration in the cards?
• What tangible benefits do you expect— will more streamlined operations and improved customer experiences count, or is revenue the only benchmark? How will you measure success?

A needs analysis highlights pertinent questions like, what level of customization will your business need?

Are Technical Resources Available?

Technical expertise on call can be a lifesaver when deploying, even if you work with the vendor or implementation partner. Though managed solutions are the vendor’s responsibility, there’s always a learning curve. Will your in-house team prove adequate, or will you need to hire externally?

What Is Your Business’ Size?

Like every other software, ETL systems aren’t one-size-fits-all. They come with varying complexity and a wide range of functionality. Software designed for small and medium-sized enterprises (SMEs) might not be able to scale as you grow.

Which solution will suit your needs?

Software designed for enterprises of all sizes is flexible and offers a rich feature set, but it can be expensive. Usage-based pricing, deployment charges and costly support packages put them out of reach of small and medium-sized enterprises.

After needs analysis, it’s time to define your key ETL requirements by working with power users, departments and key stakeholders. A ready checklist of business needs will come in handy for framing questions when reaching out to potential ETL software vendors.

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Key Requirements

Your preferred ETL tool should seamlessly extract, transform and load all information types into the designated repository, usually a warehouse. It must comply with quality standards and protect data lineage, finally delivering it to BI and analytics tools.

Here are some functional and technical requirements to consider.

1. Data Delivery Capabilities

Modern ETL tools extract information and deliver it to target repositories physically or virtually. It helps to have an ETL tool that does both since they have their uses in separate but specific scenarios.

Batch updates, change data capture (CDC), event-based ingestion and trickle-feed data acquisition convey information physically across systems and networks.

Batch processing lets you run multiple iterative jobs without human intervention. It’s useful when real-time analysis isn’t required and processing large information volumes takes priority over computing speed.

Change data capture keeps information synced and updated in repositories by capturing real-time and near-real-time changes in relational sources.

Event-based data acquisition lets you set triggers for the system to pull in information when certain events occur. Events also drive message-oriented movement — the ETL system supports application integration by exchanging data packets between systems using middleware.

Granular trickle-feed data acquisition happens in real time as new information comes in.

On the other hand, data federation, virtualization and replication enable virtual information access.

Virtualization extends warehouses with new sources and enables the federation of multiple warehouses into a single, integrated data store. It protects underlying information from changes and reduces storage requirements.

Data federation enables virtual access to enterprise applications, database structures, legacy mainframe files, text, XML and message queues. Data replication maintains a virtual copy of the source database, with data synchronization keeping it up to date.

Your preferred ETL software should be able to refresh information fully, partially or incrementally as needed.

• Data Acquisition and Delivery
• Change Data Capture
• Data Federation
• Data Replication
• Data Synchronization
• Bulk Loading
• Full Refresh
• Incremental Load
• Message-oriented Data Movement
• Partial Extraction

2. Data Quality and Governance

Quality and governance compliance must be a high-priority item to check on your requirements list when you talk to vendors. Ask if they provide a business glossary, data standardization and exception handling.

A business glossary lists the pertinent business terms, their owners and technical metadata, including tables and data management processes.

Data cleansing and enrichment supplements missing or incomplete data with relevant information acquired from external or internal sources. Data masking, or obfuscation, helps secure personally identifiable information (PII) and must be on your ask list.

Disparate information types make standardization necessary, while data matching and parsing help improve its readability. Information profiling involves collecting detailed data-related statistics from sources.

The solution must support data stewardship by providing a visual tool to configure and manage business information, rules and execution workflows, curate them and resolve integrity issues. Automated validation and exception handling are essential in an enterprise ETL tool.

Web-based reference data management with governance and semantic reference is another significant attribute.

• Business Data Glossary
• Data Cleansing
• Data Enrichment
• Data Obfuscation
• Data Matching
• Data Parsing
• Data Profiling
• Data Standardization
• Data Stewardship
• Data Validation
• Exception Handling and Management
• Reference Data Management
• Relationship Discovery

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3. Source Connectivity

Your ETL tool should connect to HTTP-enabled servers, OData services and REST endpoints in enterprise systems or third-party APIs. It should sync information effortlessly to and from the FTP and SFTP servers.

Additionally, it should have custom connectors to work seamlessly with your tech stack, including the software listed below.

• Enterprise BI platforms, including SAP Analytics Cloud, SAP BusinessObjects, IBM Cognos, Oracle Business Intelligence Enterprise Edition (OBIIEE) and Microsoft Power BI.
• Big data processing tools like Apache Hive, Cloudera Hive, Cloudera Impala, Databricks Cloud, Hortonworks, MapR Drill, Google BigQuery and Presto.
• CRM platforms, including Salesforce, HubSpot and Freshsales.
• Customer success platforms like Freshdesk, Intercom and Zendesk.
• Marketing platforms, such as Marketo, Salesforce Pardot, Oracle Eloqua, MailChimp, SurveyMonkey, Unbounce and Google Adwords.
• eCommerce software, including Shopify, Magento and BigCommerce.
• ERP software like SAP and Oracle.
• Cloud warehouses and lakes, including Snowflake, Amazon Redshift, Azure Data Lake Storage Gen 2, Azure Synapse Analytics, Databricks Delta Lake and Google BigQuery.
• Cloud computing platforms, including Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform.
• Data modeling software like Erwin Data Modeler and SAP PowerDesigner.
• Cloud storage systems, including Google Drive, OneDrive and Dropbox.
• Accounting platforms, such as Intuit QuickBooks, Freshbooks, Sage and Xero.
• Industry-standard message formats, like electronic data interchange (EDI), Health Level Seven International (HL7) and Society for Worldwide Interbank Financial Telecommunication (SWIFT).
• Mainframes and legacy databases like IMS DB, DB2 z/OS, DB2 i5/OS, VSAM and JCL.
• Massively parallel processing (MPP) databases, including Aster Data nCluster, Pivotal Greenplum and Sybase IQ.
• Message-oriented middleware (MOM) such as IBM WebSphere MQ, Microsoft Message Queuing (MSMQ), Java Message Service (JMS) and TIBCO Rendezvous.
• NoSQL databases like MongoDB, Cassandra, Apache Hbase, Couchbase, CouchDB, Redis, Riak and Amazon SimpleDB.
• Payment processing software, including PayPal, Square and Stripe.
• Project management platforms such as Jira, Asana, Trello and Mavenlink.
• Relational databases, including Oracle, SQL Server, MySQL, PostgreSQL, Sybase ASE and IBM DB2.
• SEO and web analytics tools like Adaptive Insights, Adobe Analytics Brandwatch, Crimson Hexagon, Google Analytics, Moat Analytics, Salesforce Social Studio and Heap Analytics.
• Popular social media platforms, such as Twitter, Facebook, TikTok, Instagram, YouTube, LinkedIn and Pinterest.
• Standard files, including ML Excel, MS Word, MS PowerPoint, PDF, flat files, text, CSV, XML and JSON files.
• Streaming platforms like Apache Kafka and Confluent.

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4. Data Transformation

Functions and transformation workflows are critical to preparing data for analysis.

Derived column transformation involves performing calculations on existing information by applying business rules.

Modern ETL tools perform transformations using various functions.

• These include join, lookup, filter, pivot, unpivot, sort and mathematical functions.
• You can query for string values and manipulate them using the string function.
• The rank function derives a ranking number for each value in the selected column(s) and displays it in a new column.
• The transpose function converts columns into rows and vice-versa.
• The unpivot function converts individual values from multiple columns in a single record into multiple records in a single column, retaining the same values as before.

Orchestration helps define workflows to automate and manage repetitive tasks.

• Derived Column Transformation
• Filter Function
• Deduplication
• Join Function
• Lookup Function
• Mathematical Function
• Merge Function
• Null Replacement Function
• Pivot and Unpivot Functions
• Rank Function
• Sort Function
• String Functions
• Transpose Function
• Workflow Orchestration

5. Design and Development Environment Capabilities

Many ETL tools have a built-in data viewer for a real-time look at business information. An intuitive query builder and editor supports dragging and dropping tables and columns to view the desired information.

Your ETL tool should provide the capability to configure data truncation and error-handling mechanisms for sources, transformations and destinations.

A graphical, drag-and-drop interface supports building processes, transforms, models, metadata and dataflows. Many of these are reusable, including routine workflows like cleansing, validation and enrichment.

The best ETL software has graphical toolkits to assist in testing, debugging and performance tuning during software development. Transformation object libraries help in creating and extending reusable integration objects.

Source and version control and archiving and rollback are business-critical functionalities.

• Built-in Data Viewer
• Error, Warning and Condition Handling
• Graphical User Interface
• Query Builder
• Reusable Workflows
• Testing, Debugging and Tuning
• Transformation Objects Library
• Version and Source Control

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6. Metadata Management

Metadata views should be customizable for easy visualization and comprehension. Data lineage functionality is essential to track the datasets’ journey from source to destination. Graphical tools and models help identify and define dataset relationships.

Customizable templates allow the documentation of data mapping methods. Metadata import and export are possible via a visual interface.

Modern ETL tools enable data and metadata lifecycle management from creation to ingestion, publication, retention and archival. The metadata repository is searchable — you can seek metadata related to type, name, date, keywords and subsets by folder name and even save it for later reference.

• Customizable Metadata Tree Views
• Data Lineage
• Data Model Creation and Management
• Impact Analysis
• Mapping Templates
• Metadata Import and Export
• Metadata Lifecycle Management
• Metadata Mapping
• Metadata Repository
• Metadata Search

7. Platform Capabilities

ETL platforms should enable authentication and access permissions configuration besides tracking user activity using dashboards and reports.

Activity logs capture all user actions and processes, including changes to records, and the system generates reports on demand, with the option to drill down for deeper insight.

With administrator rights, you can assign role-based access permissions to users and groups. Built-in schedulers propagate the changes made in one system to downstream systems periodically. You can set notifications to get alerts about created or deleted tasks or any change in their status.

Depending on your corporate user directory, the solution should let you configure authentication methods like single sign-on (SSO) using SAML, OpenID, Kerberos, LDAP and Active Directory. Performance dashboards enable consistent monitoring of system status. Data is encrypted at rest and in motion.

• Audit Trails
• Authentication Protocols and Systems
• Dashboards
• Data Encryption
• Data Import and Export
• Reports
• Role-based Access Control
• Scheduler
• SSO and Trusted Authentication
• System Alerts and Notifications

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How To Choose the Right System

Shortlisting your business needs in a requirements document lays down your organization’s expectations from the ETL software.

Create an ETL Requirements Checklist

Build a checklist using the above primary requirements and questions.

You can add the below considerations to the ETL requirements template.

• How frequent are the updates?
• What is the average turnaround time for support requests and issue fixes?
• How does the vendor handle unscheduled downtime?
• Which features are built-in, and which functionalities are available for a price?
• Which processes can you automate?
• How steep or easy is the learning curve? Does the vendor offer training?
• What is the deployment time?
• What customization options do they offer?

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Get Stakeholder Approval

Once the requirements list is ready, share it with senior stakeholders and teams. Google Drive and MS Office are excellent collaboration tools to get everyone’s input on priority and optional features. You can have each person rank the requirements per their individual needs in a spreadsheet or refer to our requirements management platform.

Finalize the Requirements Document

Use the priority rankings to summarize the results into a formal report and discuss them with your stakeholders. Proactive discussions can highlight which requirements are necessary and optional and how including or removing specific requirements from the list might impact your business.

Sometimes, requirements deemed critical at the onset can end up lower on the priority list, so early knowledge about your company and its operations is essential.

Reach Out to Vendors

Convert your functional and technical requirements into questions and distribute them to potential vendors. Poll the vendors based on the answers received and analyze the results to produce comparative scorecards.

Reach out to chosen vendors for demos, proof-of-concept (POC) and use cases to see how well the solution delivers on your requirements. Compare vendors based on the promised requirements and their demo scores.

Refer to our ETL comparison report for a handy feature-by-feature analysis of the market leaders.

Decide

Review the most agreeable contract and take stakeholder approval on signing up with that vendor. Sign the contract and pay the total cost of ownership (TCO) to start deployment and onboarding.

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Next Steps

Get all stakeholders on the same page early in your software search process by communicating proactively and transparently. Our ETL requirements template has pre-filled requirements for you to hit the ground running. Conducting in-depth research on in-demand ETL tools can arm you with the knowledge to contribute to the requirements checklist with your own insight.

What’s your software search process? How do you shortlist requirements? Let us know in the comments below!