How are organizations winning with Snowflake?
Cloud has evolved pretty considerably throughout the last decade, giving confidence to organizations still hoping on legacy systems for their analytical ventures. There's an excess of choices for organizations enthusiastic about their immediate or specific data management requirements.
This blog addresses anyone or any organization looking for data warehousing options that are accessible in the cloud then here you are, its Snowflake - a cloud data platform, and how it nicely fits if you are thinking of migrating to a new cloud data warehouse.
The cloud data warehouse market is a very challenging space but is also characterized by the specialized offerings of different players. Azure, AWS Redshift, SQL data warehouse, Google BigQuery are ample alternatives that are available in a rapidly advanced data warehousing market, which estimates its value over 18 billion USD.
To help get you there, let's look at some of the key ways to establish a sustainable and adaptive enterprise data warehouse with Snowflake solutions.
#1 Rebuilding
Numerous customers are moving from on-prem to cloud to ask, "Can I leverage my present infrastructure standards and best practices, such as user management and database , DevOps and security?" This brings up a valid concern about building policies from scratch, but it's essential to adapt to new technological advancements and new business opportunities. And that may, in fact, require some rebuilding. If you took an engine from a 1985 Ford and installed it in a 2019 Ferrari, would you expect the same performance?
It's essential to make choices not because "that's how we've always done it," but because those choices will assist you adopt new technology, empower, and gain agility to business processes and applications. Major areas to review involve- policies, user management, sandbox setups, data loading practices, ETL frameworks, tools, and codebase.
#2 Right Data Modelling
Snowflake serves manifold purposes: data mart, data lake, data warehouse, database and ODS. It even supports numerous modeling techniques like - Snowflake, Star, BEAM and Data Vault.
Snowflake can also support "schema on write'' and "schema on read"." This sometimes curates glitches on how to position Snowflake properly.
The solution helps to let your usage patterns predict your data model in an easy way. Think about how you foresee your business applications and data consumers leveraging data assets in Snowflake. This will assist you clarify your organization and resources to get the best result from Snowflake.
Here's an example. In complex use cases, it's usually a good practice to develop composite solutions involving:
Layer1 as Data Lake to ingest all the raw structured and semi-structured data.
Layer2 as ODS to store staged and validated data.
Layer3 as Data Warehouse for storing cleansed, categorized, normalized and transformed data.
Layer4 as Data Mart to deliver targeted data assets to end consumers and applications.
#3 Ingestion and integration
Snowflake adapts seamlessly with various data integration patterns, including batch (e.g., fixed schedule), near real-time (e.g., event-based) and real-time (e.g., streaming). To know the best pattern, collate your data loading use cases. Organizations willing to collate all the patterns—where data is recieved on a fixed basis goes via a static batch process, and easily delivered data uses dynamic patterns. Assess your data sourcing needs and delivery SLAs to track them to a proper ingestion pattern.
Also, account for your coming use cases. For instance: "data X" is received by 11am daily, so it's good to schedule a batch workflow running at 11am, right? But what if instead it is ingested by an event-based workflow—won't this deliver data faster, improve your SLA, convert static dependency and avoid efforts when delays happen to an automated mechanism? Try to think as much as you can through different scenarios.
Once integration patterns are known, ETL tooling comes next. Snowflake supports many integration partners and tools such as Informatica, Talend, Matillion, Polestar solutions, Snaplogic, and more. Many of them have also formed a native connector with Snowflake. And also, Snowflake supports no-tool integration using open source languages such as Python.
To choose the prompt integration platform, calculate these tools against your processing requirements, data volume, and usage. Also, examine if it could process in memory and perform SQL push down (leveraging Snowflake warehouse for processing). Push down technique is excellent help on Big Data use cases, as it eliminates the bottleneck with the tool's memory.
#4 Managing Snowflake
Here are a few things to know after Snowflake is up and running: Security practices. Establish strong security practices for your organization—leverage Snowflake role-based access control (RBAC) over Discretionary Access Control (DAC). Snowflake also supports SSO and federated authentication, merging with third-party services such as Active Directory and Oakta.
Access management. Identify user groups, privileges, and needed roles to define a hierarchical structure for your applications and users.
Resource monitors. Snowflake offers infinitely compute and scalable storage. The tradeoff is that organizations must establish monitoring and control protocols to keep your operating budget under control. The two primary comes here is:
Snowflake Cloud Data Warehouse configuration. It's typically best to curate different Snowflake Warehouses for each user, business area, group, or application. This assists to manage billing and chargeback when required. To further govern, assign roles specific to Warehouse actions (monitor, access/ update / create) so that only designed users can alter or develop the warehouse.
Billing alerts assist with monitoring and making the right actions at the right time. Define Resource Monitors to assist monitor your cost and avoid billing overage. You can customize these alerts and activities based on disparate threshold scenarios. Actions range from suspending a warehouse to simple email warnings.
Final Thoughts
If you have an IoT solutions database or a diverse data ecosystem, you will need a cloud-based data warehouse that gives scalability, ease of use, and infinite expansion. And you will require a data integration solution that is optimized for cloud operation. Using Stitch to extract and load data makes migration simple, and users can run transformations on data stored within Snowflake.
As a Snowflake Partner, we help organizations assess their data management requirements & quantify their storage needs. If you have an on-premise DW, our data & cloud experts help you migrate without any downtime or loss of data or logic. Further, our snowflake solutions enables data analysis & visualization for quick decision-making to maximize the returns on your investment.