Key Takeaways: 

  1. Snowflake offers a single data cloud platform to manage various data workloads. The platform includes a data warehouse, data lake, data sharing features, data applications, data engineering and data science capabilities.  
  1. By using Snowflake, you’re able to eliminate all data silos and have ONE data cloud platform for data from various sources – both internal and external.  
  1. Our data engineers often recommend the use of Snowflake in BI stacks. Over the last few years, Snowflake has become the most sought-after solution for data warehousing and data lake capabilities, especially in scenarios where there are several input sources. 
  1. With Snowflake, you can easily access and analyse data from SaaS applications, on-premise applications, external data sources, ERP, CRM, cloud storage tools and databases.  

How Western Union otpimised data visualisation with Snowflake 

Western Union, a leading financial services provider, serves 250 million customers across retail and digital channels. It ingests and analyses a large volume of transactional data. Insights from this high-volume data are used to constantly improve customer and agent experience. 

However, Western Union’s legacy data architecture, consisting of several on-premise data warehouses, was unable to provide a comprehensive view of all operational data from 250 million+ customers across locations.  

It was not easy to visualise data, provision users, ensure 24/7 uptime, and perform maintenance across its locations. The lack of a modern data cloud platform was certainly a bottleneck from a business intelligence perspective.  

The company implemented a multi-cloud strategy with Snowflake at its core and was able to experience: 

  • 50% lower TCO on data warehousing  
  • Manage 150 million customers across retail and digital channels globally, even as their data was being used to garner insights 
  • Consolidate more than 30 data stores into Snowflake 
  • Save millions of dollars annually by efficiently gathering more insights from data  

Snowflake: A Robust Data Cloud Platform for Various Data Workloads 

Snowflake, a cloud-native data warehousing solution, provides storage and analytics services. It also integrates seamlessly with platforms such as Google Cloud, Amazon S3, and Microsoft Azure.  

Snowflake enables enterprise-wide secure data sharing from the cloud, eliminating the need for a separate hardware appliance, installation, configuration, or management.  

With Snowflake, businesses do not need separate data warehouses, data marts, and data lakes. It can handle multiple workloads, and is flexible and efficient, allowing data to be moved easily into Snowflake using a solution such as Stitch or FiveTran for ETL. 

The Snowflake architecture decouples storage and compute capabilities, allowing them to scale independently as needed, thereby making it cost-efficient. Snowflake has a hybrid architecture. Like the traditional shared-disk architecture, all compute nodes can access a central data repository that stores persisted data. It also provides the advantage of shared-nothing architecture, by processing queries using MPP (massively parallel processing) compute clusters. As a result, a portion of all the data sets is stored locally. This hybrid approach makes data management simple while providing performance and scale-out capabilities. 

Unique Capabilities of Snowflake 

Snowflake helps with quick ingestion, transformation, and delivery of data for informed decision-making. It does not need infrastructure planning and management, allowing businesses to focus on innovation. Some of its features include: 

  • Real-Time Data Access: Through batch and continuous ingestion of structured, semi-structured, and unstructured data, Snowflake provides all data consumers with access to live data. 
  • Greater Pipeline Reliability and Performance: Optimize performance, improve reliability of data processing, and save costs by running elastic, dedicated, and appropriately-sized pipelines. Pipelines are nothing but a secure pathway to move data from various sources into the Snowflake platform. 
  • Simple Pipeline Architecture: Reduce clusters by streamlining architecture and pipeline development using SQL and Snowpark for extensible pipelines. 

Why Data Teams Love Snowflake 

Snowflake is a cloud-native solution that provides unlimited scalability, facilitates data transformation with ease, and allows large volumes of queries. The five benefits of deploying Snowflake include: 

Benefit #1: Elasticity  

Snowflake provides elasticity to scale up and down compute power when there’s an increase in the volume of queries or if the data needs to be loaded more quickly. This elasticity is a game changer for data teams, looking to unify data from multiple sources. With Snowflake, there is a seamless data cloud that acts as a bridge between data sources and data consumers, irrespective of scale.  

Benefit #2: Advanced analytics on both structured and semi-structured data 

Today businesses need structured as well as semi-structured data. Snowflake allows both types of data to be stored and processed for analysis without converting or transforming them into a fixed relational schema. This is a key feature, especially for advanced analytics. Data consumers don’t care about the type of input data. They are looking for high-quality insights, which can be presented only if all data points are taken into consideration.  

Benefit #3: Multicluster architecture for concurrency 

Snowflake uses multicluster architecture to enable a large number of concurrent users or use cases to access resources. As the virtual warehouse can scale up or down based on need, data engineers can access any data without waiting and run concurrent queries without impeding others.  

This is also a key feature of a modern data cloud. Different business teams across the world may want to access data from different parts of the enterprise. With Snowflake, accessing, processing and analysing this data concurrently is fairly seamless.  

Benefit #4: Ease of data sharing 

Users can share data seamlessly with other data consumers by providing reader accounts created from within the Snowflake platform. This is also a key feature where user authentication and authorisation are taken into account before sharing.  

Additionally, business teams don’t want to be bogged down by technical details or processes. They want data at their fingertips with little to no reliance on IT. The Snowflake platform is built to enable just that.  

Benefit #5: Availability and security 

Snowflake is SOC 2 Type II certified, supports PHI data for HIPAA customers, and allows encryption during communications across networks. A distributed system, it is highly tolerant of component and network failures and can operate continuously with minimal disruptions. 

Sample Use Cases of Snowflake 

Snowflake has several use cases, including: 

  • Simplifies cloud migration 
  • Provides scalable storage solution 
  • Facilitates faster reporting on large volumes of data 
  • Enables data analysis at scale  
  • Leverages agile methodology 
  • Simplifies data processing 
  • Ability to analyse several types of data structures such as CSVs, Avro, XML, JSON, and Parquet. All these can also be blended using SQL language 

Merit Group’s expertise in cloud BI and data cloud 

At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, Glenigan, and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities. 

Our specific services include high-volume data collection, data transformation using AI and ML, web watching, BI, and customized application development. 

We’re experts in Cloud BI, helping companies streamline and migrate to a truly next-generation BI stack. 

Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our expertise in data engineering is especially useful in this context. Our data engineering team brings to fore specific expertise in a wide range of data tools including Airflow, Kafka, Python, PostgreSQL, MongoDB, Apache Spark, Snowflake, Redshift, Athena, Looker, and BigQuery. 

Our team of data engineers can help you with faster time-to-insights using Snowflake, which is ideal to bring in data from multiple sources.  

At Merit Group, our data and business intelligence teams can offer strategic guidance on building the right data ecosystem, custom designed for your business. We’ll also help you choose the right data cloud platform based on your volume and/or types of data to be processed. 

If you’d like to learn more about our service offerings or speak to a Snowflake expert, please contact us here: 

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