Building your Modern Data Platform in the Cloud
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Legacy Data Platform Migration Services
As enterprises embrace the era of AI and analytics, many are seeking to modernize their data platforms by migrating from legacy systems to Cloud Data Platforms. This shift promises significant advantages including eliminating the challenges of maintaining and operating outdated platforms, i.e., high licensing costs, technical debts, and limitations in handling unstructured data.
These platforms also offer scalability and the opportunity to harness a broad array of modern analytics and AI tools for innovative business use cases. A robust platform should efficiently manage large and diverse datasets from multiple sources, enabling collaborative analysis, diverse data exploration, and timely decision-making capabilities.
But, migrating legacy data platforms like Data Warehouses, Hadoop Platforms/On-Premises Data Lakes, and Business Warehouses, to modern Cloud data platforms can be challenging due to the complexities of legacy data formats, jobs and workflows and even the amount of data to be migrated. Also, Enterprises have built data transformation code over decades in traditional ETL or data processing tools/technologies. Migrating the transformation code to modern open-source tools and Cloud-native services needs a combination of automation and accelerated proven processes.
Lemongrass brings extensive expertise in Cloud workload migration, specializing in the seamless transfer of critical workloads, including legacy data. Our approach is differentiated by our unique Cloud migration methodology, automated Data Lake and Landing Zone setup, Continuous Integration/Continuous Deployment (CI/CD) processes, and best practices in Cloud Financial Operations (FinOps) and Security Operations (SecOps).
This approach includes four distinct phases designed to ensure that the migration is seamless and is accomplished with the least amount of downtime and disruption.
Legacy Data Platform Migration Pilot: The pilot phase of Legacy Data Platform Migration services plays a critical role in risk mitigation by testing the migration process on a small scale before full implementation. This approach helps in the early identification and resolution of potential issues ensuring a smoother transition in subsequent stages.
A preliminary architecture is designed and implemented to validate the chosen migration approach. This step ensures that the architecture meets essential criteria such as scalability, performance, and security requirements. Selected use cases are migrated to the target platforms during this phase, allowing for a controlled and manageable evaluation of the migration process. This testing phase assesses the feasibility of the migration and helps in identifying and addressing any challenges early in the process.
The success of the pilot phase is evaluated against predefined success criteria to determine whether the objectives of the pilot have been achieved. This evaluation process ensures that the migration approach is robust and well-prepared for broader implementation across the organization.
Architecture, Design and Technical Foundation Phase: Establishing a strong architecture and technical foundation is pivotal for ensuring the effectiveness of a new Cloud environment, guaranteeing security, scalability, and robust capabilities for data management and analytics. The process involves designing a comprehensive architecture and technical framework tailored to the Cloud, incorporating stringent security measures, effective data governance practices, and scalability considerations.
This phase also encompasses the evaluation and deployment of the chosen data platform, whether it be AWS Redshift, Google Cloud BigQuery, Azure Synapse, Snowflake, or Databricks, to meet specific organizational needs.
Creating a secure and scalable data lake or data platform on the Cloud is essential, accommodating various data types and ingestion patterns effectively. Implementing continuous integration and continuous deployment (CI/CD) processes further enhances efficiency by automating infrastructure setup and streamlining data pipeline management throughout the lifecycle.
Implementation and Testing Phase: This phase includes transferring data from legacy systems to Cloud-based platforms to ensure data integrity and minimize downtime and migrating applications like jobs, workflows, ETL pipelines, and ML models to the Cloud for continued functionality. It also includes thorough testing to verify seamless operation in the new environment while also adhering to performance, security, and governance standards and comprehensive testing, including systems integration, user acceptance, and performance testing, to ensure the migrated environment meets business requirements.
Production Deployment/Parallel Run Phase: Running the new system in parallel with the old one provides a safety net, ensuring that any issues can be quickly resolved without disrupting business operations. It also ensures a smoother transition and increased confidence in the new platform and is essential for any big critical legacy data platform migration.
Cutover Phase: The cutover phase marks the final step in the migration process. This phase ensures that the legacy systems are decommissioned, and all operations are fully supported by the new Cloud-based platform, completing the migration journey. It also ensures all data consumers (reporting systems, APIs, etc.) are pointed to the new platform for data/information consumption.
