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What You'll Learn
If you’re still relying on Hadoop for your data processing and reporting requirements, chances are you are facing challenges that are complex and expensive to address. Hadoop-based systems like Apache Spark, Hive, and others are difficult to manage. For example, Hive does not have any database lying underneath and this makes it difficult to govern data, perform data modifications and real-time changes.
Data engineering techniques that worked wonders during the Apache Hive and Hadoop era no longer match the current evolving user requirements. It’s difficult to find professionals who are experienced in these obsolete technologies and can maintain them.
Companies across domains are moving to cloud solutions and so should you. We recommend you migrate from Hadoop to Snowflake – the leading AI data cloud platform.
In this blog post, we will cover the architectural differences between Hadoop and Snowflake, why moving from Hadoop to Snowflake is beneficial, the best migration plan, and important steps to follow. Read on.
Understanding the Architectural Differences Between Hadoop and Snowflake
Hadoop is an open-source framework and supports distributed compute and processing of datasets. It is based on the MapReduce programming model and is primarily used for batch processing. Snowflake is a cloud-native data platform and offers a Software-as-a-Service model to customers. The architecture is designed to handle structured and semi-structured data.
On the other hand, Hadoop Distributed File System can deal with unstructured data. However, if you need to use the data for analytical purposes, you require extra tools. Hadoop has a cluster-based setup, and you require tools like YARN for job scheduling and resource management.
Snowflake provides native support for SQL and automatic clustering. In Hadoop, horizontal scaling is possible by adding nodes to the cluster. Snowflake auto-scales clusters to handle varying workloads.
Why Move from Hadoop to Snowflake
There are several reasons to migrate from Hadoop to Snowflake.
- Whether you’re managing on-premises or cloud-based Hadoop clusters, you require a dedicated team with technical expertise to handle upgrades and any other task related to Hadoop’s underlying infrastructure. One thing to note is that nowadays it’s difficult and highly expensive to find such technical expertise.
- Hadoop doesn’t integrate well with modern, cloud-native architecture. As its design is primarily focused on batch processing, users looking for real-time analytics might not find it suitable for their business.
- Another major reason is scalability issues. Hadoop manages growing data volumes by adding more nodes to the cluster. Managing a large cluster with thousands of nodes can become challenging. Users will find themselves experiencing performance and an operational overhead increase with scale, especially in terms of data replication and monitoring.
Modernizing from Hadoop to the Snowflake platform is one of the best investments you could make this year. You work with a software-as-a-service platform that provides you with a pay-as-you-use pricing model.
Snowflake works well with all sorts of data and allows you to easily share data securely within as well as outside your organization. Thanks to its zero copy cloning feature, you can create multiple copies of tables, schemas, or databases without copying the data.
Find a quick overview of the Snowflake architecture and details on the benefits of using the platform here.
Hadoop to Snowflake Migration Plan – Key Phases Explained
We recommend planning and executing your migration plan over three phases – the discovery phase, deployment phase, and validation phase. Let’s look at them one by one.
1. Discovery Phase
This step involves garnering and documenting all the critical background information about your current Hadoop environment and identifying dependencies.
This information document should include the types of tools and technologies you have been using, your data sources, use cases, integrations, resources, and some end-user training programs.
Breakdown of all the points mentioned above:
- Tools & Technologies: Here you mention all the tools and technologies you have been using for your Hadoop environment – both Hadoop-native and third-party tools. Together with a Snowflake migration specialist like LumenData, you need to identify what tools are needed going forward and which ones will be replaced by Snowflake. The tools that you would be taking forward with you need to be re-evaluated to check if they will be able to function properly in Snowflake’s cloud-native environment. You will also be required to evaluate SQL queries and processing workloads to check their compatibility with Snowflake.
- Source of your Data: You have both internal and external data sources with you. In the discovery phase of your Hadoop to Snowflake migration journey, you need to make sure that for every data source, you have one subject matter expert who has a thorough understanding of that particular data source or data set.
- Hadoop Use Cases: Next you need to carefully study the applications running in your Hadoop ecosystem. There are various tools that are leveraged for building any application. So, the deployment for each of these applications would be different. We recommend creating a list of applications – explain their types, reference architectures, names of tools and technologies being used, and readiness for the Snowflake environment.
- Integrations: When you move from Hadoop to Snowflake, it is important to list all the applications that use data from your Hadoop system. This should be done to ensure that the applications can access data in the new Snowflake environment. There are some applications that have complex ways of verifying users and controlling access. Such applications need to be reviewed in the discovery phase.
- End-User Training: Current users of the Hadoop system might think of the migration process as a cumbersome task. We advise you to make your end-users familiar with the Snowflake environment by arranging and conducting necessary training sessions for them.
All the information that you collect in your discovery phase sets the tone for your migration success.
2. Deployment Phase
This is the phase where you move your applications from Hadoop to Snowflake. Now, you don’t move your data sources, tools, or applications all at once. You need to identify which ones need to be prioritized.
We recommend creating a list of information stored in the Hadoop Distributed File System and then moving it to storage offered by the cloud. While moving the data, you need to keep the folder and file organization the same as it was in HDFS.
Please note that the deployment phase will be the longest and, maybe, the most complex phase in your entire migration journey. At LumenData, we automate many parts of your migration journey and make it as seamless as possible.
3. Validation Phase
This phase is to test the outcomes of the migration from Hadoop to Snowflake. This is where you confirm whether the data and processes in the new system work as expected.
The validation stage covers it all – data validation, query validation, SQL validation, governance validation, and user acceptance testing.
Last but not least, business validation is done. Here, a final check is done to verify if the migration to Snowflake aligns with the organization’s requirements.
Key Steps for Migrating from Hadoop to Snowflake
Quick summary of the key steps involved in Snowflake migration projects:
Step 1
Assessment of Current Data Architecture
Step 2
Finalization of the Migration Approach
Step 3
Setting up the Snowflake Environment
Step 4
Data Loading into Snowflake
Step 5
Optimization of the Migration Performed
Step 6
Continuous Performance Reviews
We have covered this in detail here. Also, learn about the best approaches you can implement for Snowflake migration. Example: Lift and shift, lift, fix, & land, and complete redesign.
Turn your Migration Project Successful with LumenData
Migration can be complex and take more time than usual. Consider seeking help from LumenData with expertise and years of hands-on experience. We have unique and proprietary offerings and accelerators that reduce your migration time by two months.
As a Snowflake Premier Services Partner, our Snowflake capabilities include Snowpipe for data ingestion, Snowpark for data transformation and machine learning, Snow Tasks & Procedures for scheduling the data processing, Streamlit for data visualization and reporting, and many more.
The LumenData Advantage
75+ Snowflake certifications – SnowPro Advanced, Advanced Architect, Advanced Data Engineer, Technical Sales Pro.
6–12-week QuickStart program tailored for healthcare, financial services, retail, public sector, manufacturing, higher education.
On-demand and scheduled support from subject-matter experts.
Accelerators for rapid data warehouse migration from legacy, superpipe for high-speed data ingestion, &data governance accelerators.
Our expertise in Snowflake deployments is not limited to migration. We help form a modern data strategy that has the best of Snowflake and other leading data technologies. For example: Snowflake combined with Informatica Implementations. We have done it for several large organizations.
One of them is a leading managed vision care company that had traditional on-prem ETL Databases and a batch-based ecosystem, and struggled with inconsistent data across departments, regulatory compliance risks, etc. We helped them with the migration of 5 data domains to the Snowflake Cloud Data Warehouse and achieve end-to-end HIPAA compliance. You can find more information here.
Our experts can also help you with Gen AI-enabled migration to Snowflake from Cloudera, Redshift, and other on-prem data systems. Connect today to learn more.
About LumenData:
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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