Why Migrate from Hadoop to Snowflake

Hadoop has served as Big Data. It uses the Hadoop Distributed File System (HDFS) and stores the data on-premises by breaking it into smaller sets. Hadoop platforms have not achieved business value and suffer from high maintenance costs, poor performance, and a lack of advanced data science capabilities. So enterprises are looking to transition to more reliable cloud-based platforms. Snowflake offers the following benefits.

• Performance Improvements: Hadoop requires constant tuning, whereas Snowflake provides significantly faster processing and query performance. Due to its unique architecture that helps Snowflake to separate compute from storage, it can be scaled up or down. It manages optimization, caching, and resource allocation. 
• Cloud-native architecture: Hadoop’s scaling requires manual cluster expansion, and it is more complex to manage. Snowflake is built from the ground up as a cloud-native data warehouse with separate storage and computing layers. 
• Ease of use: Snowflake supports full ACID compliance and is optimized for both traditional and modern analytics. It enables simple data sharing across organizations.
• Cost reduction: Hadoop requires a dedicated, skilled team to install, configure, and manage the ecosystem. This increases the high total cost of ownership (TCO). Snowflake is easy to install and maintain. All the configuration things are handled automatically, which allows teams to focus on data and analytics.

Things to Consider Before Migrating from Hadoop to Snowflake

For a successful Hadoop to Snowflake migration, we need to consider the following critical areas.

• Architecture and Data strategy: Identify the critical data to be moved to Snowflake. Define the new Role-based Access Control security model and plan for file optimization during migration. Clean and retire outdated datasets to avoid unnecessary storage in Snowflake.
• Check compatibility: Hadoop’s workloads, such as MapReduce logic, Hive queries, and Spark transformations, require redesign. Check the compatibility of Hive tables with Snowflake SQL queries.
• Cloud storage: Snowflake is a cloud-native platform that might rely on AWS, Azure Blob, or Google Cloud Storage for data ingestion. Choose the storage layer that aligns with your business needs and budget.
• Schema mapping and optimization: Direct schema mapping of Hive or HDFS is not available for Snowflake. Review file formats, partitions, indexes, and data types. Plan for schema conversion to align with Snowflake’s architecture and performance best practices.
• Security and compliance: Snowflake’s built-in security features require mapping. Evaluate the roles based on access and governance needs before migrating sensitive data.
• Cost: Snowflake uses a consumption model. Calculate expected costs for compute, storage, and data transfers. This will avoid unexpected expenses post-migration.

How to Migrate from Hadoop to Snowflake(Step-by-Step)

A successful migration requires thorough planning, goal setting, and a migration strategy. Addressing the steps below will help you avoid errors and achieve performance when moving to a new base.

1. Planning and Assessment:

This is the foundational step for the Hadoop to Snowflake migration. Analyze the current Hadoop environment and document existing data sources, ETL processes, analytical queries, reporting tools, HDFS storage, Hive table structure, and integrations. Assess workloads to determine critical data assets and prioritize them for migration. Create a roadmap with a detailed migration plan, timelines, budget, roles, and milestones that cover schema, data, and application migration. Find out which data or workloads are to be first. This prioritization manages complexity and reduces migration risks.

2. Set up Snowflake:

Set up the Snowflake accounts, users, roles, virtual warehouses, and cloud storage locations. Create cloud storage integration to stage data. Establish a snowpipe or other ingestion pipeline for automated data loading. Ensure that access controls, roles, and permissions, and governance standards are compliant from the start.

3. Schema Migration:

Start by extracting the metadata from Hive metastore that includes tables, views, columns, and partitions. Translate this into Snowflake-compatible DDL. This may involve manual conversion for complex structures or utilizing tools for automated translation. Create necessary tables, views, and database objects in Snowflake based on DDL. 

4. Stage data in Cloud storage:

Upload extracted files into cloud storage. This staging area acts as a bridge between Hadoop and Snowflake. Organize files based on schemas and workloads.

5. Data Migration:

Use Snowflake’s data loading tools, such as the COPY command, Snowpipe, or ETL platforms. Load data into Snowflake tables via bulk loads or Snowpipe continuous ingestion.

6. Rebuild or Redesign ELT/ETL Workflows:

Specific jobs of Hadoop, such as MapReduce logic, Hive scripts, and Spark transformation, require redesign. Rewrite them using Snowflake SQL or try to replace them with ETL tools.

7. Tables and Queries translation:

Now it is time to convert the Hive SQL queries and ETL scripts into compatible Snowflake SQL. Manually or use automated tools to translate HiveQL scripts to SnowSQL. Rewrite data processing jobs, stored procedures, and the dbt tool for transformations.

8. Validation and Testing:

Thoroughly do rigorous testing to ensure data accuracy and consistency between Hadoop and Snowflake. Validate the functionality and performance of migrated workloads.

What to Do After You Migrate from Hadoop to Snowflake

Some post-migration steps to be considered are 

• Monitor and optimize: Continuously monitor the performance and optimize workloads on Snowflake. Review slow-running queries and tune the corresponding virtual warehouses. 
• Cost governance: Set up Resource Monitors to alert on or suspend compute consumption. Analyze which users and queries consume more credits to enforce accountability.
• Security and Compliance: Configure the roles, masking policies, data retention rules, auditing, and encryption settings. Snowflake’s built-in governance features help to maintain compliance with organizational and regulatory standards.
• Training and documentation: This is one of the most important post-migration steps to consider for future growth. Ensure that the team is educated on the new platform and provide ongoing support. Once validation is complete, make the BI tools point to Snowflake.
• Decommissioning Hadoop: After successful migration, decommission Hadoop clusters incrementally to realize the full OpEx savings of the migration.

Why choose Entrans for your Hadoop to Snowflake Migration

Migration is not about transferring data; it is about reliability and performance. Our team at Entrans specializes in migrating from Hadoop to Snowflake services. Our team of experts is dedicated to ensuring security and transparency with a proven track record.

Entrans comes with a wide range of engineers specialized in migration skill sets. Our specialists have a wide knowledge and are well-versed in both Hadoop and Snowflake. We manage the whole process for you by securely moving your content to the cloud platform.

If you are planning a Hadoop to Snowflake migration, our team is here to support and ensure a smooth and efficient transition. Want to know more about it? Book a free consultation call!