One of the most considerable challenges for a data platform owner today is upgrading their data platform infrastructure. We found a way to automate the conversion of several legacy technologies to Snowflake by autoconverting them to dbt projects. Here’s how we did it.

Migrating systems like Apache Hadoop — mainly Apache Hive and Apache Spark — to more modern and efficient platforms like Snowflake can be complex and time-consuming, often requiring significant resources and expertise. The process can also impose a significant cost on human capital. However, with the right tools and strategies, this process can be significantly accelerated and streamlined.

A recent webinar we hosted discussed how companies have tackled this challenge using our internal tool, Kali. This blog post will summarize the key points from the webinar, providing insights into the benefits of migrating from Apache Hive to Snowflake, the challenges involved, and how Tropos’ Kali framework can help accelerate this process.

The Problem Statement

A scattered ecosystem

The primary reason for migrating from Apache Hadoop to Snowflake is the growing difficulty in sustaining Hadoop-based systems. Hadoop’s ecosystem, which includes Apache Spark, Hive, and others, is increasingly seen as a scattered landscape of various technologies that are challenging to manage and maintain. Unlike traditional databases, Hive does not have a database lying underneath, making it difficult to govern data at scale in a cost-efficient way.

Migrating data is like moving house — you can do it yourself, but having a professional team makes it quicker and less stressful.

The skills gap

Furthermore, data engineering practices that were effective during the era of Hadoop and Hive do not align with today’s standards. This discrepancy creates a skills gap, making it harder to find professionals who are both experienced in these older technologies and capable of maintaining them.

An evolution of best practices

Additionally, migrating from Hive to another platform is not a straightforward process due to the unique engineering practices and design patterns ingrained in Hive’s ecosystem.

Planning and Governing the Migration

Tropos has developed a structured roadmap to manage these migrations, dividing the process into three main phases: preparation, re-platformation, and redesign.

Step 1: Preparation

This phase involves creating a comprehensive inventory of data pipelines. Automation plays a crucial role here, helping to assess which pipelines can be migrated automatically and which ones require manual intervention. This upfront analysis allows for better planning and resource allocation, ensuring that complex tasks are identified early in the process.

Step 2: Re-platformation

Once the data is available in Snowflake, the focus shifts to code conversion. This step is one of the most labor-intensive parts of the migration, as it involves translating the business logic from Hive to Snowflake-compatible SQL. The goal is to replicate the output of the as-is pipelines without changing the underlying data model. This phase also includes extensive code testing to ensure that the migrated pipelines produce the same results as the original ones.

Step 3: Redesign

The final phase involves optimizing and modernizing the data model and pipeline architecture to take full advantage of Snowflake’s capabilities. This step often includes re-engineering data models and integrating new features and best practices.

Automating the Migration

Tropos’ Kali framework is designed to automate significant portions of this migration process, especially the tedious and repetitive tasks. The framework can automate code conversion from Hive to Snowflake SQL, identify and replace obsolete design patterns, and generate a new dbt (data build tool) project structure optimized for Snowflake.

The framework consists of several components:

• Interpreter Plugin: This component automates the conversion of Hive SQL dialect to Snowflake SQL, replacing Hive-specific functions with their Snowflake equivalents.
• Pattern Handler: The pattern handler identifies and converts outdated Hive design patterns into modern dbt-compatible practices. This feature is particularly useful for dealing with non-standard SQL operations, such as those involving complex data transformations and orchestration logic.
• dbt Builder: After converting the SQL code, the dbt builder generates a complete dbt project, including models, macros, and configuration files necessary for running and managing the data transformation pipelines in Snowflake.
• Visualization Tool: Kali also provides a visualization tool that offers a graphical representation of the data pipelines, helping teams understand the structure and dependencies within their data workflows. This visualization is invaluable for spotting inefficiencies and optimizing the migration process.

Challenges and Caveats

Despite its capabilities, the Kali framework is not a silver bullet. Some challenges remain, particularly with code generators and highly customized pipeline logic that cannot be easily automated. For these cases, manual intervention is required to rebuild the code generator in a more modern framework like dbt, which can then be automated in future migrations.

Another critical aspect is the orchestration of data pipelines. While Kali can help transform the data transformation logic, integrating this with existing orchestration tools like Apache Airflow or other newer tools may require additional effort and customization.

The Impact and Future of Platform Migration Automation

We have seen significant success using Kali in previous migrations. In one case, a migration that initially took 60 person-days was reduced to 32 seconds using Kali. This dramatic reduction in time underscores the potential of automation in data migration projects. On average, Tropos has achieved a 74% faster conversion rate and an 82% faster analysis time using their framework.

Looking ahead, we plan to make Kali available through the Snowflake marketplace, allowing other organizations to leverage this tool for their migration projects. This move aims to broaden the accessibility of Kali, enabling more companies to accelerate their transition from legacy data systems to modern cloud-based platforms like Snowflake.

Conclusion

Migrating from Apache Hive to Snowflake is a complex but necessary step for organizations looking to modernize their data infrastructure. While the challenges are significant, the benefits of such a migration are substantial, including improved data governance, cost efficiency, and the ability to leverage modern data engineering practices.

Tropos’ Kali framework represents a powerful tool in this journey, offering automation and efficiency that can drastically reduce the time and effort required for migration. As data landscapes continue to evolve, tools like Kali will be invaluable in helping organizations keep pace with technological advancements and remain competitive in the data-driven economy.

For organizations considering a migration, engaging with experienced partners like Tropos and utilizing advanced tools like Kali can make a significant difference in the success and speed of the transition. As the data industry continues to grow and change, staying ahead of these trends will be crucial for maintaining a competitive edge.

As an experienced cloud engineering partner, Tropos helps companies develop new data products and platforms in Snowflake and dbt using best practices regarding cost management and efficiency right from the start. We also help build your business case to migrate existing architecture towards a sustainable cloud environment.

Our diversified team of data engineers, business consultants and analysts will translate your requirements into actionable data products, tailored to your needs. Don’t hesitate to reach out for more info!