As many businesses struggle to make sense of all their data, choosing a data platform that can handle enormous volumes of big data, fast speeds, and reliability—not to mention the ease of use—is their first priority.
As part of a long-term strategy commitment to transform into a cloud-first, data-driven company, many already use cloud data platforms or are considering doing so. Snowflake, a strong contender with almost 20% market share, supports multiple cloud infrastructures, including those from Amazon, Microsoft, and Google Cloud Platform (GCP).
Snowflake: Origin, Evolution and Importance
One of the most well-known Software-as-a-Service (SaaS) cloud data warehouses, Snowflake was founded in 2012. It allows data storage and computing to scale independently and is constructed on top of the infrastructure of the following cloud platforms:
- Amazon Web Services (AWS)
- Azure Cloud by Microsoft
- Cloud Platform by Google (GCP)
It can be used for data lakes, operational data stores, data warehouses, and data solutions because it is a multi-purpose cloud data warehouse. It provides data ingestion, storage, processing, and analytical solutions that are far more rapid, user-friendly, and adaptable than other alternatives.
Performance and operational cost are balanced by its decoupled compute and storage architecture with automatic up and down scalability. Snowflake offers outstanding manageability for data warehousing, data lakes, data analytics, data access and governance.
The Top Eight Use Cases of Snowflake
Use case 1: Session Transactions and Data Storage
Session data is present in enormous amounts of business settings. But not all problems with data analysis are related to quantity. Data must be maintained up to date and fresh.
Abstraction: Snowflake’s processing abstraction combined with warehouses enables auto-scaling of compute capacity to match the needs of the company without modifying the infrastructure.
Role-based access: Users can encrypt personally identifiable information (PII) data or restrict the fields that are accessible in Snowflake by using Secured Views.
Backups: Snowflake’s Time Travel function creates 90-day backups that are regularly stored. If some problem occurs, you can easily roll back to a previous version of the data set or even undrop a table.
Use case 2: Data Operations in Machine-Learning Environments
To precisely forecast developments in the financial industry, you can devise solutions leveraging previous market and trade data, news, and legislation data with machine learning. Snowflake provides capabilities including SnowSQL for manually curated data entry, Snowpark for user-defined functions in Java, JavaScript, Python and SQL, and multi-cluster warehouses for processing several queries with high load at once.
Use case 3: Seamless Ingestion and Integrations
Snowflake adapts to different data integration patterns, such as batch, near real time, and real time, without any issues. Numerous integration vendors and technologies like Informatica are supported by Snowflake. Additionally, many of these have created a native connector for Snowflake. Snowflake also allows no-tool integration using Python and other open source languages.
Use case 4: Business Intelligence
Data warehousing is a critical part of any business intelligence operation. Using Snowflake, your organization can implement data warehouses faster, which can be used for ad-hoc analysis by running SQL queries. Snowflake can easily be integrated with business intelligence tools such as Looker, PowerBI, QuickSight, and Tableau.
Use case 5: Machine Learning
A lot of businesses employ machine learning algorithms to forecast data. Data engineers must construct, test, and choose the best attributes for their ML models as part of feature engineering. To support each experiment, complete datasets must be copied, which may be done with Snowflake’s zero-copy cloning function. Apache Spark may also be used and directly connected to Snowflake. Programming languages that are used for machine learning, like Python, R, Java, and C++, are supported by Snowflake.
Use case 6: Data Security and Governance
Data security and governance are crucial when dealing with sensitive business and customer data. Snowflake offers integration with a variety of third-party data governance technologies, including Immuta, Collibra, Informatica, and many others. When compared to conventional data warehouses, Snowflake helps keep performance high while lowering operational costs.
Use case 7: Powerful Data Processing
Data scientists must have access to sufficient compute power to evaluate and prepare the data before putting it into sophisticated machine learning models and deep learning tools. Snowflake’s innovative design offers a separate computing cluster for each job and team. Data processing with SQL can boost speed and efficiency by up to 10 times while opening feature engineering to a wider range of data experts.
Use case 8: Micro-Partitioned Data Storage
Unlike typical static partitioning, all data in Snowflake tables is automatically partitioned into micro-partitions, which are contiguous storage units. The data size of a micro-partition varies from 50 MB to 500 MB. The most efficient compression algorithm is automatically allocated to each column of each micro-partition by Snowflake’s Storage Layer.
Questions to Answer before and after You Choose Snowflake
1. What challenges are you aiming to solve?
Snowflake has a distinctive scalable architecture that divides its computation and storage resources and is created specifically for the cloud. Snowflake’s multi-cluster, shared data architecture consists of three layers:
- Compute Layer, sometimes referred to as Virtual Warehouse, is a scalable, elastic, and resizable layer.
- Holding Layer (leverages hybrid columnar, compressed storage mechanism)
- Services Layer (takes care of all metadata, security, and optimization aspects of data management)
2. What methods are currently in place for data loading?
The Snowflake Web Interface is the easiest way to do data input into Snowflake. Snowpipe can also be used to automate large data loading. Using the Snowflake CLI and SQL commands in SnowSQL, you can bulk load massive amounts of data.
3. How to Manage Snowflake?
Once Snowflake is operational, you must look into security measures, access control, and resource monitors. Snowflake enables SSO and federated authentication, interacting with third-party systems. For every system user, business region, or software, it is typically better to construct a dedicated Snowflake Warehouse to manage chargebacks and separate billing.
Conclusion
An enterprise would need to make a large investment in IT tools and knowledge in order to have the storage capacity and compute power that Snowflake offers out-of-the-box. Companies across a range of industries must improve their data platforms in order to take advantage of new and forthcoming tools and apps. They have better access to their data and more advanced analytics with Snowflake, which help them grow their businesses.
To transform the data ingestion, storage, compliance and sharing for your business, talk to us.