What Is a Data Cloud? And Other Snowflake Enhancements

Are you a business or technology professional? Get near-infinite and flexible scalability to load, analyze, integrate easily, and securely share the data using Snowflake. Snowflake is a completely managed service that is simple to use but can power an almost unlimited number of simultaneous workloads. It is a customer solution for data engineering, data warehousing, data lakes, data application development, data science, and sharing securely and consuming the shared data. It has unique features as a result of these features, it has quickly become a leader in data management solutions for analytics. So attending Snowflake Training and learning it will be one of the best choices that you can make to enhance your career. Let us know about Snowflake, data cloud, and the new enhancement announced by Snowflake through this post.

What is Snowflake?

Snowflake is a cloud-based data warehouse solution that provides companies with scalable and flexible storage while at the same time hosting a solution for business intelligence. It is built on top of AWS. It provides a faster, easy to set up, and far more flexible data warehouse. It has a unique architecture that allows the users to create tables and start data querying with lesser administration or Database administration activities required. It is the first analytics database that was built in the cloud and delivers data warehouse as a service. It can run on Azure, AWS, and Google cloud platforms. To install, configure and manage Snowflake, there is no requirement of any hardware or software. It entirely runs on public cloud infrastructure.

What is a data cloud?

Data cloud enables the organizations to combine and connect to a single copy of all their data easily. This results in an ecosystem that connects the organizations with each other, which can consume and share data and data services. Data centers are used by cloud storage with huge computer servers which physically store the data and make it available online through the web. Users can upload their content remotely and can retrieve or store them when needed.

What industries think about data warehouses was changed by Snowflake with its cloud-native offering. So it was adopted by many organizations. So, Snowflake company wants to move further with the introduction of data cloud, which is the company positions as a single-window where the organizations can execute data-oriented tasks like data warehousing, SQL analytics, data engineering, machine learning, and monetization of third party data. The origin of the data cloud concept with the increase of public clouds from Azure, AWS, and google cloud, which Snowflake will be at the top. At the same time, the rise of application cloud and Saas applications from the likes of Workday, SAP, and Salesforce have offered transactional data to process on the cloud. But it's much easier to put transactional data in the public cloud so that they can work together in a seamless way. All these application clouds and data centers have not helped to solve the problem that we have been facing for generations called Siloing of data. There are numerous problems with data silos. One of the problems is - it is very difficult to join, integrate or augment data from various silos. This prevents us from understanding and gaining critical insights. Fragmented and Proliferated data is another data governance problem, and increasing privacy and security becomes very difficult. This is an intolerable scenario for the companies that value their brand. So they end up with huge expenses. These data storage and integration problems led to the data lake.

Snowflake Cloud Data Platform becomes the center of an organization's data operations. When the companies shift to Snowflake cloud, they will be away from governance and integration problems. Customers need not move all their data into snowflakes to get the benefits of the data cloud. It is attempting to extend its influence beyond the cloud and into its customers' data centers. Some of the New enhancements that aim at helping the customers to access, manage, share and benefit from are:

• Snowflake data marketplace: It allows the data scientists, analytics, and business intelligence professionals access to live ready-to-query data sets from external data and service providers. It also provides a broad range of open and commercial data sets across the categories like public health, demographics, weather, and software-as-a-service providers. Snowflake also made it easy for marketplace providers to update their profiles and add new providers among them.

• Data providers can easily update snowflake data marketplace profiles without the need to republish existing lists or create a new profile. When the provider updates the profile, it is approved by Snowflake and automatically updated for all the organization's existing publishing list.

• For data consumers, Snowflake data Marketplace continues to increase the depth and breadth of data sets available.

• Governance: It provides leading data governance and security with good controls to offer consistent policies and protections. With the announcement of general availability for column-level protection, Snowflake continued to improve its capabilities. This allows the users to protect sensitive data like protected health information (PHI) and protected identifiable information (PII) by applying masking columns to table columns or views. 

• Dynamic data masking feature- for authorized users, sensitive data in plain text is dynamically masked at the time of the query.

• External Tokenization feature- It tokenizes the sensitive data before loading it into Snowflake and detokenizes it dynamically for the authorized user at the time of the query.

• Extensibility: Modern analytical workloads often require complex transformations or augmentations which need the use of custom codes or third-party applications. However, the use of external services and libraries frequently complicates data pipelines. To simplify the use of remote services, the External function feature was created by Snowflake. It allows the users to call external APIs and custom code from Snowflake and mix the result into the query result. Customers can use external functions in external tokenization, geocoding, machine learning scoring, and various additional use cases.

• Expansion: The snowflake platform uniquely extends multiple regions and clouds. It helps the customers to run their workloads where they want and also offers new levels of connectivity of data and availability. As Snowflake continues to expand to new regions, data can be processed and stored where the customers require it.

• Manageability: As a public preview, snowflake Organization offers a central view of all the business accounts, like usage details and billing. For simplifying account management and billing, Snowflake secure data sharing, data replication, and failover/failback, and other account administration tasks, Organizations give you quick access to the information about your accounts contract, rate information, remaining balance, hourly credit use of warehouse the previous year and daily consumption offering you with a real-time view into your current usage and speed.

• Snowflake added a new page called Monitors in the user interface by which we can manage resource monitors to create, drop and modify resource monitors, view details, or control access to snowflake resources. Resource Monitor objects help organizations to manage costs and avoid unexpected credit usage created by running warehouses. You can also impose limits on the number of credits consumed by user-managed virtual warehouses and virtual warehouses used by the cloud using resource monitors.

• Performance: Point lookups are frequent on many Snowflake workloads from data exploration use cases. The data scientists will run some ad hoc queries to make sense of a data set given to the application workloads where analysts require quick access to dashboards to find helpful information from massive amounts of data. Snowflakes' new search optimization service allows the users to analyze billions of rows, giving them quick insights with updated data. To identify the complex patterns quickly using SQL, the MATCH_RECOGNIZE clause of Snowflake will help us. This statement is used to identify rows that match a particular pattern removing the requirement of adding multiple joins. This feature offers an easy and faster way to analyze web traffic, understand what leads to customers placing items in their cart, or identifying security stocks or threats that have particular behavior.

• Ecosystem: Snowflake works with various industry-leading tools and technologies that allow the customers to access the platform through an extensive ecosystem of connectors, programming languages, drivers, and tools. Snowflake also announced improved support for ODBC, Node.js, Go, and .NET drivers and also includes multi-factor authentication token caching for ODBC drivers that allows an application to use multi-factor authentication of Snowflake, minimizing the number of prompts. 

• Snowflake connector for python has new functionality to speed up large batch insert operations, streaming data to a temporary stage for ingestion automatically when the values that are to be inserted exceed a threshold.

• Data pipeline: The continuous loading of data hosted on public cloud storage services into Snowflake accounts by automating Snowpipe, Snowflake's data ingestion service. Snowflake also announced the availability of support for snow pipe data that is automated loads into snowflake accounts hosted on AWS from data files either on Azure or GCS.

All the above are the enhancements that were released in February and March 2021. These new enhancements of Snowflake aim at helping the customers to access, manage, share and benefit by using Snowflake. Snowflake will probably remain the leading data cloud business for the foreseeable future. So learning it will definitely enhance your career.