More and more companies are looking at cloud migration.
Migrating legacy data to public, private or hybrid clouds provide creative and sustainable ways for organizations to increase their speed to insights for digital transformation, modernize and scale their processing and storage capabilities, better manage and reduce costs, encourage remote collaboration, and enhance security, support and disaster recovery.
But let’s be honest – no one likes to move. So if you’re going to move from your data from on-premise legacy data stores and warehouse systems to the cloud, you should do it right the first time. And as you make this transition, you need to understand what data you have, know where it is located, and govern it along the way.
Automated Cloud Migration
Historically, moving legacy data to the cloud hasn’t been easy or fast.
As organizations look to migrate their data from legacy on-prem systems to cloud platforms, they want to do so quickly and precisely while ensuring the quality and overall governance of that data.
The first step in this process is converting the physical table structures themselves. Then you must bulk load the legacy data. No less daunting, your next step is to re-point or even re-platform your data movement processes.
Without automation, this is a time-consuming and expensive undertaking. And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative.
By using automated and repeatable capabilities, you can quickly and safely migrate data to the cloud and govern it along the way.
But transforming and migrating enterprise data to the cloud is only half the story – once there, it needs to be governed for completeness and compliance. That means your cloud data assets must be available for use by the right people for the right purposes to maximize their security, quality and value.
Why You Need Cloud Data Governance
Companies everywhere are building innovative business applications to support their customers, partners and employees and are increasingly migrating from legacy to cloud environments. But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy.
The desire to modernize technology, over time, leads to acquiring many different systems with various data entry points and transformation rules for data as it moves into and across the organization.
These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (APIs), file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.
With all these diverse metadata sources, it is difficult to understand the complicated web they form much less get a simple visual flow of data lineage and impact analysis.
Regulatory compliance is also a major driver of data governance (e.g., GDPR, CCPA, HIPAA, SOX, PIC DSS). While progress has been made, enterprises are still grappling with the challenges of deploying comprehensive and sustainable data governance, including reliance on mostly manual processes for data mapping, data cataloging and data lineage.
Introducing erwin Cloud Catalyst
erwin just announced the release of erwin Cloud Catalyst, a suite of automated cloud migration and data governance software and services. It helps organizations quickly and precisely migrate their data from legacy, on-premise databases to the cloud and then govern those data assets throughout their lifecycle.
Only erwin provides software and services that automate the complete cloud migration and data governance lifecycle – from the reverse-engineering and transformation of legacy systems and ETL/ELT code to moving bulk data to cataloging and auto generating lineage. The metadata-driven suite automatically finds, models, ingests, catalogs and governs cloud data assets.
erwin Cloud Catalyst is comprised of erwin Data Modeler (erwin DM), erwin Data Intelligence (erwin DI) and erwin Smart Data Connectors, working together to simplify and accelerate cloud migration by removing barriers, reducing risks and decreasing time to value for your investments in these modern systems, such Snowflake, Microsoft Azure and Google Cloud.
We start with an assessment of your cloud migration strategy to determine what automation and optimization opportunities exist. Then we deliver an automation roadmap and design the appropriate smart data connectors to help your IT services team achieve your future-state cloud architecture, including accelerating data ingestion and ETL conversion.
Once your data reaches the cloud, you’ll have deep and detailed metadata management with full data governance, data lineage and impact analysis. With erwin Cloud Catalyst, you automate these data governance steps:
- Harvest and catalog cloud data: erwin DM and erwin DI’s Metadata Manager natively scans RDBMS sources to catalog/document data assets.
- Model cloud data structures: erwin DM converts, modifies and models the new cloud data structures.
- Map data movement: erwin DI’s Mapping Manager defines data movement and transformation requirements via drag-and-drop functionality.
- Generate source code: erwin DI’s automation framework generates data migration source code for any ETL/ELT SDK.
- Test migrated data: erwin DI’s automation framework generates test cases and validation source code to test migrated data.
- Govern cloud data: erwin DI gives cloud data assets business context and meaning through the Business Glossary Manager, as well as policies and rules for use.
- Distribute cloud data: erwin DI’s Business User Portal provides self-service access to cloud data asset discovery and reporting tools.
Request an erwin Cloud Catalyst assessment.
And don’t forget to register for erwin Insights 2020 on October 13-14, with sessions on Snowflake, Microsoft and data lake initiatives powered by erwin Cloud Catalyst.
