Technically the data was migrated - but operationally it’s clearly a failure. This sounds like an easy question to answer at first glance. Who defines the rules for data quality? Do you need to consolidate your systems following a merger or acquisition? Being early adopters we had already embraced Hadoop on several of our projects. I hope this article was helpful. Carefully evaluate the capabilities that your organization needs, and make sure you choose a vendor that can deliver. By searching using a variety of filters, you can discover how your content is being stored and how it can be organized further. Post data migration/data lake ingestion a very common acceptance criteria from the customer is to perform data verification. Through the last few years, it has been my primary objective to develop a solution that not only is fast but also low cost. It isn’t always possible or practical to complete all these steps manually, so be sure to link up with an experienced technology partner. That means combing through all shared drives and workflows to determine where and how your data—structured or not—is used. The next time you’re putting together a data migration project plan, make sure you give more than a fleeting moment to the topic of data migration success criteria. Post data migration/data lake ingestion a very common acceptance criteria from the customer is to perform data verification. This is especially true when dealing with heterogeneous databases. This step isn’t exclusive to born-digital content, though. The length of the design phase depends mainly on the need to acquire additional migration tools and the need to develop extraction, loading, and quality checking software. Take the time to assess your data rules and ensure they’re working the way they’re supposed to, in addition to mapping out any exceptions to your data routing. Very frequently time spent on data verification surpasses the time it took to actually perform the migration or ingestion. Dylan has an extensive information management background and is a prolific publisher of expert articles and tutorials on all manner of data related initiatives. Once you’ve developed your data migration strategy, the next step is to assess the content you have. We’ll define user stories upfront because acceptance criteria are written after we’ve specified all functionality through user stories. So we need more rules and specifications to define the “right data.” Not always easy when we’re literally dealing with a moving target. Next, timing is everything. Following are the stages of evolution of the framework that I have developed over the years. Keeping the history of updates in the new system is as important as keeping the lineage of migrated data, particularly if they have been merged from different legacy sources. Take a look. Great reminder, Dylan, that migrating data is more than just "moving" them into a new technical environment. Of course we in the Data Migration end of things need these exact details to perform our data migration. Your content is now fully accessible and can be leveraged for various organizational needs, whether you wish to reduce manual effort, fuel big data analytics, improve data security, tighten up compliance, or all of the above. As a first step, get clear on what business objectives your migration project will facilitate. I have found several companies, none of which are in Las Vegas. Even though I have not posted the entire code set and commands feel free to reach out to me if you require additional details. Here is how we did it: I hope this article helps you in performing fast yet low cost data verification. Before you know it, you’ll be well on your way to improving your data before you move it. So we know that data quality is a criteria for success. Our data has to meet the specifications of the target system interfaces, application logic and user requirements. Accelerate Your Transition to Intelligent Data, Find and Extract Data from Complex Documents, Any Format, Any Source to Searchable PDFs, Get the latest market trends, insights and POVs to build a smarter business. I am a Student in the Networking field. In my early days, a very common method of data validation was sampling. The sample set was visually scanned/compared by a quality assurance engineer. Although a full-scale data migration project might seem intimidating at first, breaking it down into smaller chunks makes it much more manageable. If this cannot be guaranteed in the "batch" migration process, migrated data have to be flagged accordingly to warn "consuming" processes. In this step, you can begin sorting your content into relevant categories. The course is taught online by myself on weekends. Delta Lake is covered as part of the Big Data Hadoop, Spark & Kafka course offered by Datafence Cloud Academy. Many organizations are under the impression that a data migration is a simple undertaking. Some solutions—but not all—will allow you to redact confidential information or personally identifiable information (PII). Once you’ve completed the proof-of-concept stage and are confident in the solution you’ve chosen, it’s time to get your team up to speed. The problem grows exponentially if you need to perform several iterations of migration and ingestion. Data Migration Checklist: The Definitive Guide to Planning Your Next Data Migration Coming up with a data migration checklist for your data migration project is one of the most challenging tasks, particularly for the uninitiated.. To help you, we've compiled a list of 'must-do' activities below that have been found to be essential to successful data migration planning activities. Make learning your daily ritual. This meant that sampling of data is no more an option. Other possibilities in this step include compressing your content, enhancing metadata for greater searchability, and converting it into PDF or another universal format.