From Grounded to Clouded: Why IOblend is Your Fast-Track to the Cloud
Today, we talk about data migration. Data migration these days mainly means moving to the cloud. Basically, if a business wants to drastically improve their data capabilities, they have to be on the cloud. Data migration is the mechanism that gets you there.
For example, when Rakuten Rewards migrated its on-prem Hadoop clusters to AWS, using services like Amazon S3, Amazon Elastic MapReduce (EMR), and Snowflake. This migration reduced their complexity and improved operations, allowing them to build scalable systems quickly and at a fraction of the previous cost.
This process is far from straightforward, unfortunately. Corporate complexity means there is a whole world of challenges involved. Data migration takes multiple phases, requires high expertise, and is ridden with challenges that derail even the most robust plans. Let’s see how it works.
What Does Data Migration Involve?
Data migration is not about lifting and shifting your current information to the cloud. Just want to state that.
It’s about transforming and optimising it for the new destination systems. There is little point in dumping everything you have on-prem to a cloud lake and thinking the job’s done. All you’ll do is create a data swamp and waste money on managing the sprawl.
You need to do careful prep work, figure out the business goals, assess what data you have and use, and choose a target platform that will suit your strategy best. Data preparation follows, involving cleaning and formatting the data to remove redundancies and ensure compatibility.
Once prepared, the data undergoes mapping and transformation, a critical phase where its structure is aligned to match the target system’s requirements. The actual transfer then takes place using migration tools (or manual processes). It’s then followed by testing to make sure all data has been accurately moved and is operational in the new environment. Finally, you need to monitor the system post-migration for performance and compliance issues.
How Long Does It Take To Migrate Data?
Everyone and their dog. Joking. But there are a lot of stakeholders involved, usually the IT and SMEs who own the data/systems. Internal IT teams would normally lead the project, working alongside cloud service providers (e.g. AWS or Microsoft Azure). Third-party consultants or vendors like ourselves at IOblend are frequently engaged to handle complex migrations.
They key is to get all parties rowing in the same direction. Data migration projects are not just about moving bits and bytes. They are deeply human undertakings. The worst thing that can happen to any data migration project is misalignment, unclear goals, and lack of communication among the stakeholders. This is a sure way to make your data migration eye-wateringly expensive and achieve little.
Who Manages Data Migration?
A consultant’s answer here: it depends. The complexity of the source and target systems, the volume of data, and the industry’s regulatory requirements will define that. Small businesses with minimal data might complete migration within weeks, while larger enterprises could require several months or even years. Spotify’s migration of petabytes of data to Google Cloud took approximately two years.
For highly regulated industries such as healthcare, timelines are often longer. A UK-based healthcare provider migrating to a hybrid cloud environment discovered there was a two-year timeline due to stringent compliance checks and legacy system challenges. Double its initial estimates…
If you set a clear strategy and align the stakeholders, the next biggest challenges are technical. Selecting the right partners for tooling and advice can greatly accelerate the timescales (and keep the costs down).
Costs of Data Migration
Speaking of which. The costs associated with data migration vary widely. Small projects may cost as little as £5,000-10,000, while enterprise-level migrations involving terabytes of data and complex integrations can exceed £1 million. We’ve seen a (failed) migration project that took three years and cost eight figures with little to show for. Ouch. The worst thing with this is that the issue doesn’t go away – they still need to do the migration.
Tools and systems are not the biggest cost in this process. Sure, the good ones don’t come for free. But the biggest hit comes from the developer time – the longer you need to code pipelines, do validations, clean data, rebuild processes, etc., the more it’ll cost you. If you need a small army to deliver the complexity, it’ll cost you more. Get the strategy wrong and change the requirements half-way? Be prepared to cough up even more ££.
And you get hit on the upside too. If you’re sitting waiting to migrate to the cloud, you are not getting its benefits. Both, on the cost reduction side and the revenue uplift.
No wonder, every business dreads enterprise migration projects.
Common Challenges
One of the most critical challenges is maintaining data integrity and security. Businesses must ensure that data is neither lost nor corrupted during the migration process. Security concerns become somewhat prominent when sensitive customer or financial data is involved, as breaches lead to expensive regulatory penalties and reputational damage.
Then there is a risk of downtime. Extended periods of unavailability during migration can disrupt daily operations and lead to customer dissatisfaction.
Compatibility issues with legacy systems also pose risks. Older systems do not always want to play nice with modern cloud architectures (or other on-prem systems for that reason), calling for costly adjustments and redevelopment.
In 2018, TSB Bank attempted to migrate customer data from its former parent company, Lloyds Banking Group, to a new system. The migration was poorly organised and controlled, which resulted in widespread service outages. Customers couldn’t access their accounts, and some reported seeing other customers’ data. They got hit with significant reputational damage and financial penalties from the regulator.
Mitigating Challenges and Maximising Success
To get migration right, you must prioritise comprehensive planning, involve key stakeholders and employ the right tools for the job. Leveraging advanced solutions like IOblend for technical parts of the integration can greatly simplify and de-risk complex projects.
For instance, you can do parallelised migration, where data, processes and business rules are deployed while the old system is still in BAU. You can deploy individual processes in prod and switch off the old ones once you are satisfied with the outcome. There is no disruption to the end services.
Engaging experienced consultants can provide the expertise needed for seamless execution, especially for enterprises lacking in-house resources.
Then, make sure you invest proper time and effort in employee training so that the teams run with the new systems.
What IOblend Does For Data Migration
This (conveniently) brings me to our solution. Data migrations are one of our core use cases. We built the tool to be able to handle any integration challenge you can throw at it.
In summary, IOblend speeds up, simplifies, and enhances the robustness of data migration through a combination of automation, flexibility, and comprehensive feature sets. We also do not force you to conform to a rigid template or architecture: tailor the design to your specific needs.
Automation Of Data Management
IOblend automates many aspects of data integration and pipeline management.
How this helps: Automation eliminates repetitive manual tasks, such as configuring pipelines, handling data transformations, or managing metadata. This significantly reduces the time required for each stage of the migration, enabling faster project delivery.
Example: Instead of writing extensive scripts to transfer, manage and transform data, teams can rely on IOblend’s automated features to handle these processes in the background, reducing errors and increasing reliability.
Low/No-Code Development
The tool offers a low/no-code interface for designing and managing data pipelines.
How this helps: By just using SQL and Python, it allows users with varying technical expertise to participate in the migration process. All aspects of Spark (coding, translations, data management) are taken care of automatically. This speeds up development cycles and reduces dependence on specialised developers.
Example: Data engineers can easily and quickly set up pipeline, enabling non-technical stakeholders to understand and validate workflows without requiring deep coding expertise.
Versatility Across Data Architectures
IOblend supports both centralized (data lakes, warehouses) and federated (data meshes, multi-source systems) architectures.
How this helps: Companies can choose the architecture that best suits their requirements, without needing a rework to fit into the tool’s constraints. This flexibility ensures seamless integration with existing systems. Swiss Army knife.
Example: A hybrid setup with some data stored on-premises and some in the cloud can easily be accommodated, making the migration smoother. Or integrating streaming and batch data in the same pipeline the same SQL you would do for pure batch.
Compatibility with Diverse Environments
The platform integrates effortlessly with local, on-premises, edge, cloud, and hybrid environments.
How this helps: This compatibility means companies don’t have to change their operational setups significantly during migration, reducing the complexity and potential downtime.
Example: A retail company using edge computing for real-time customer analytics can migrate its data to a cloud-based analytics platform without disrupting its edge systems.
Comprehensive Data Source and Sink Integration
IOblend connects to virtually all data sources and destinations using standardised protocols like JDBC, APIs, and dataframes.
How this helps: This wide compatibility ensures that no matter how diverse a company’s data sources or storage systems are, they can all be seamlessly integrated into the migration pipeline.
Example: A business with legacy databases, SaaS applications, and flat-file data storage can consolidate its data into a modern cloud warehouse like Snowflake without needing separate tools.
Built-in Production-Grade Features
IOblend includes advanced features like Change Data Capture (CDC), SCD, schema management, event-driven processing, time travel, asset management, error handing, and de-duplication.
How this helps: These features ensure that the migrated data is not only accurate but also updated in real time. Robust error handling, lineage tracking, and version control provide transparency and security, reducing the risk of issues post-migration.
Example: A financial institution can migrate its customer data while maintaining historical records and capturing real-time updates, ensuring the migration aligns with compliance and operational requirements.
Accelerated Testing and Validation
With in-built tools to automate testing and validation, IOblend ensures that data integrity is preserved during and after migration.
How this helps: Validation tools identify mismatches or errors early in the process, reducing delays caused by data discrepancies.
Example: Post-migration testing can automatically compare source and destination data to verify that all records have been accurately transferred and transformed.
Streamlined Management Post-Migration
IOblend includes monitoring, cataloguing, and metadata management tools that simplify post-migration operations.
How this helps: Once data is migrated, businesses can continue to manage, query, and audit data effectively using IOblend’s built-in tools. This reduces the need for additional platforms or personnel.
Example: After migrating to the cloud, a retail company can use IOblend’s cataloguing tools to create a searchable inventory of its data assets, improving accessibility for analytics teams.
Why IOblend Makes Data Migration Simpler
Speed: Cut the migration implementation phases of the process by over 50%.
Reduced Risk: Robust error handling, version control, and real-time lineage tracking provide confidence in the data migration process.
Efficiency Gains: Automation and compatibility with diverse systems minimise bottlenecks, allowing teams to focus on strategic aspects rather than operational details.
We designed the tool to address the common pain points of data migration projects, such as complexity, time constraints, and risk of errors. By automating processes, supporting multiple architectures, and providing advanced tools, we let you migrate data faster, more reliably, and with greater simplicity.
If you want to learn more about it, get in touch. We are happy to tell you all about how we approach the challenge of data migration.
IOblend presents a ground-breaking approach to IoT and data integration, revolutionizing the way businesses handle their data. It’s an all-in-one data integration accelerator, boasting real-time, production-grade, managed Apache Spark™ data pipelines that can be set up in mere minutes. This facilitates a massive acceleration in data migration projects, whether from on-prem to cloud or between clouds, thanks to its low code/no code development and automated data management and governance.
IOblend also simplifies the integration of streaming and batch data through Kappa architecture, significantly boosting the efficiency of operational analytics and MLOps. Its system enables the robust and cost-effective delivery of both centralized and federated data architectures, with low latency and massively parallelized data processing, capable of handling over 10 million transactions per second. Additionally, IOblend integrates seamlessly with leading cloud services like Snowflake and Microsoft Azure, underscoring its versatility and broad applicability in various data environments.
At its core, IOblend is an end-to-end enterprise data integration solution built with DataOps capability. It stands out as a versatile ETL product for building and managing data estates with high-grade data flows. The platform powers operational analytics and AI initiatives, drastically reducing the costs and development efforts associated with data projects and data science ventures. It’s engineered to connect to any source, perform in-memory transformations of streaming and batch data, and direct the results to any destination with minimal effort.
IOblend’s use cases are diverse and impactful. It streams live data from factories to automated forecasting models and channels data from IoT sensors to real-time monitoring applications, enabling automated decision-making based on live inputs and historical statistics. Additionally, it handles the movement of production-grade streaming and batch data to and from cloud data warehouses and lakes, powers data exchanges, and feeds applications with data that adheres to complex business rules and governance policies.
The platform comprises two core components: the IOblend Designer and the IOblend Engine. The IOblend Designer is a desktop GUI used for designing, building, and testing data pipeline DAGs, producing metadata that describes the data pipelines. The IOblend Engine, the heart of the system, converts this metadata into Spark streaming jobs executed on any Spark cluster. Available in Developer and Enterprise suites, IOblend supports both local and remote engine operations, catering to a wide range of development and operational needs. It also facilitates collaborative development and pipeline versioning, making it a robust tool for modern data management and analytics
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