What is real-time data?

Real-time data refers to information that is processed and made available for immediate use. This type of data is updated continuously and consistently, allowing organisations to make informed decisions based on the most recent information available.

In today’s fast-paced VUCA-centric world (Volatile, Uncertain, Complex, Ambiguous), businesses need to be agile to stay competitive. Real-time data allows organisations to perform real-time analytics, so they can address customer needs quickly, effectively and accurately.

Benefits of real-time data

Improved decision-making, efficiency and optimisation

It allows organisations to make informed decisions and real-time adjustments based on data almost immediately after it becomes available. By utilising real-time data and integrating it correctly, organisations can respond to changing market conditions and customer needs swiftly. The integration of real-time data can also significantly reduce the risk of making decisions based on outdated information

For example, in the hospitality and travel industry, real-time data of happenings in specific countries can lead to higher demand in flights or accommodations instantly. A new world tour announcement from any major musician will lead to a high demand in flights and hotels from hardcore fans who travel to follow their tour. Organisations in these industries can then access real-time public datasets of events in the vicinity and make adjustments to the accommodations offered for better business outcomes.

Enhanced customer experience

It can help organisations to better understand their customers and their needs. By analysing customer behaviour and preferences in real-time, organisations can provide a more personalised and relevant experience to improve customer satisfaction and loyalty.

For example, in the hospitality industry, some businesses utilise wall-mounted tablets in washrooms that allow their customers to rate the cleanliness of the facility. With real-time data being sent and analysed, an employee can be dispatched immediately to perform cleaning of the washrooms which were rated poorly.

Improved collaboration

It can improve collaboration between departments within an organisation for better business outcomes.

For example, in the hospitality industry, using up-to-date information on check-ins or last-minute cancellations at the front desk, can be used by other departments to ensure rooms are cleaned or booked by those on a waiting list. This can improve the customers experience and allow the team to work more efficiently and increase profit.

Better planning and forecasting

It can help organisations to make accurate planning and forecasting decisions.

For example, in the sales industry, real-time sales data allows organisations to make more accurate predictions about future sales trends and make informed decisions about resource allocation.

Reduced costs

It allows organisations to reduce costs by automating processes and making real-time adjustments to improve efficiency.

For example, in the transportation industry, real-time traffic data can be used to optimise routes and reduce fuel consumption, reducing operating costs and leaving behind a greener carbon footprint.

Enhanced competition

It helps organisations to stay ahead of the competition by allowing them to respond to changing market conditions and customer needs quickly and effectively. With real-time data, organisations can make informed decisions based on the latest information available, giving them a competitive advantage over organisations that make decisions on older, outdated data.

Now that we understand the concept of real-time data and its benefits, how does Snowflake Data Cloud fit in?

Introduction to Snowflake Data Cloud

Snowflake enables organisations to mobilise their data with their Data Cloud. Customers use the Data Cloud to unite siloed data, discover and securely share data, execute diverse analytic workloads and facilitate real-time data. With Snowflake, organisations can now process and analyse massive amounts of data with ease, enabling them to make informed decisions in real-time.

Some Benefits of Snowflake

Ability to handle both structured and semi-structured data

Traditional on-premise data warehouses are only capable of handling structured data, making it difficult for organisations to store and analyse data from sources such as social media and the internet of things (IoT). With Snowflake, organisations can store and analyse a wide range of data types, including JSON, XML and Parquet, in a single platform. This makes it easier for them to gain insights into their data and make informed decisions based on the information they gather.

Scalability

Snowflake is designed to scale on demand, allowing organisations to handle increasing amounts of data as their needs grow. This makes it an ideal solution for businesses with rapidly growing data needs, such as those in the healthcare, financial services and retail industries. Snowflake also offers a pay-as-you-go pricing model, which means only paying for the resources they use.

Robust security features

…to protect sensitive data. The platform uses encryption and multi-factor authentication to protect data in transit and at rest. Snowflake has implemented strict security protocols and has undergone rigorous security audits to ensure the safety of customer data. With Snowflake, organisations can be confident that their data is secure and protected.

Ability to support real-time analytics

Snowflake uses a combination of data warehousing and big data technologies to enable processing and analysis of massive amounts of data in real-time. This means that organisations can make informed decisions based on the latest data, enabling them to respond to changing market conditions and customer needs quickly and effectively.

Now that we have learnt about the flexibility, scalability, security and ability to support real-time analytics, we will focus on how Snowflake supports real-time analytics by consuming real-time data.

How Snowflake Data Cloud enables real-time data analytics with real-time data

Data Ingestion

Snowflake supports multiple forms of ingestions that facilitate consumption for real-time analytics through BI & self-service reporting tools. These reporting tools can be configured to send automated alerts when a certain metrics hits a certain threshold.

Some examples of these ingestions include:

• Batch / Micro-Batch Scheduled Processing
• Snowpipe Kafka
• External Tables
• Data Share

Snowflake provides the flexibility to incorporate both traditional scheduling of batch processing of data which optimises and saves resources. They also allow real-time processing and ingestion of incoming data streams without batch scheduling, based on data freshness requirements.

Data Transformation

Using the Snowflake Steams & Tasks feature, we can receive the data, perform change data capture, transform and store data ready for consumption for analysis by Business Intelligence tools such as Tableau.

Snowflake can process incoming data streams without batch scheduling and scale up, down or out and perform the Extraction, Loading and Transformation (ELT) automatically in real-time.

For example, if there is a dataset which needs calculations, aggregation or business logic to be added prior to loading into the final table for consumption and reporting, Snowflake tasks automatically execute a SQL statement or stored procedure once it detects a push from the stream.

Multi-Cluster Architecture

…is unique and differentiates Snowflake. It can seamlessly handle multiple concurrent users and dynamically auto scale up or down based on the queries or resources needed.

Advantages of Snowflake Architecture

Simplicity

Using a pipeline to capture, implement change data capture and storage can be completed with just a few lines of SQL statements.

This allows data engineers and data analysts with existing data knowledge of SQL to quickly perform the tasks needed to ingest, transform and visualise their data in real-time, removing the need for upskilling in different programming languages or nuances.

Streamlining

With all data transformation performed in the Snowflake transformation component, there will be no code duplication or various different technologies needed to achieve the transformation outcome. It provides an all-in-one solution for SQL transformation.

This allows business rules or transformations to be stored within Snowflake and reduces redundancies, making it a more streamlined and effective option.

Elasticity

With real-time data, spikes in workloads can be irregular and unpredictable.

Snowflake’s elastic architecture enables users to add more compute power on-demand, without any downtime or disruption to the system. This allows Snowflake to handle spikes in workloads autonomously and transparently to the users.

Concurrency

Real-time data will not only be used by a single person or entity, but will usually be used by a team consisting of data engineers, data analyst and even across departments in bigger organisations.

Snowflake’s architecture allows for high levels of concurrency which means that multiple users can run queries and access data simultaneously without affecting the performance of the system. This is possible as Snowflake automatically manages resources to allocate them efficiently to users.

Real-time data considerations

Real-time vs Non-real time data

In an ideal world, we want the latest datasets at all times to be able to perform immediate operational decision making. We will need to consider datasets which are not key to be in real-time vs data which are important in real-time.

For example, in a Supermarket chain, items which are sold may not need to be updated in real-time and instead inventory can be updated in bulk at the end of the day. Using Snowflake Stream, we can perform the calculation and tasks needed to update an inventory list but Snowflake can also perform scheduled tasks on a daily basis to update the item list all at once.

Integrations and connectors

While Snowflake focuses on its architecture and not on the ingestion and transformation portion for real-time data, they do have pre-built connectors. One such connector is Kafka Connect, which provides features such as data validation, cleansing and normalisation of real-time data which reduces the development time of the data. By leveraging on these pre-built connectors, data quality can be improved, data integrations are streamlined and real-time data processing can be enabled, all within Snowflake.

Summary

Real-time data is a fantastic tool for organisations looking to stay competitive and respond to changing market conditions and customer needs quickly and effectively. It can improve decision-making, increase efficiency, enhance the customer experience, improve collaboration, enrich planning and forecasting, reduce costs and give businesses a competitive edge. Real-time data and analytics are assets for organisations of all sizes and industries. By leveraging real-time data, organisations can turn their data into a competitive advantage and drive growth and success.

Snowflake facilitates this by providing an architecture which is scalable, secure and flexible in handling multiple types of data types. Ready to enable real-time data? Get in contact below to speak to our experts.