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Why Data Latency Matters

As the need for real-time and near real-time data access grows, data latency has become increasingly important. Reducing the amount of time it takes to access data doesn’t just mean quicker access to data, but also faster time to insight and the ability to act on that insight immediately. In this article, we explore why data latency has such an impact and how low data latency benefits businesses. We will also look at how Snowflake removes many of the traditional barriers to low data latency. 

What Is Data Latency and Why Does It Matter?

Data latency is the measure of the time it takes for data to become available in a database, data warehouse, or data lake after the data-generating event has occurred. Think of latency like an echo in a canyon: Latency is similar to the amount of time that passes from the moment someone yells “Hello!” to the time the echo returns. Data latency is typically measured in seconds or milliseconds.

Low latency is important for two reasons. First, it provides users with a better experience. For example, if a user is gaming online, completing a purchase on a retailer’s website, or conducting a trade, small delays can have an outsized impact. Second, low data latency is quickly becoming a necessity as the adoption of advanced, data-dependent technologies such as predictive analytics programs and cybersecurity platforms become foundational to business. 

How Optimizing Data Latency Benefits Businesses

Lowering data latency improves the performance of many business processes, enabling organizations to take advantage of time-sensitive opportunities. Here are a few of the many ways reducing data latency can benefit businesses. 

Advance cybersecurity operations

Security information and event management (SIEM) systems and security data lakes rely on rapid access to data as it’s created. Low data latency enables security teams to quickly detect security events and mount an appropriate response.  

Improve inventory control

Online retailers require low latency to accurately sync supply and demand. Lower data latency also helps improve the user experience, resulting in near-instant responses to actions taken on the platform.

Automate manufacturing processes

Real-time control over automated processes on the factory floor depends on ultra-low latency. Manufacturers must be able to dynamically adjust to conditions in the moment for efficient operations and profitability. 

Enhance retargeting and product recommendations for online retailers

Low data latency can help retailers more effectively engage their customers. When targeting online shoppers, timing is critically important. Long lag times are detrimental for retargeting and product recommendations based on what was recently added to a digital shopping cart. Lower latency allows retailers to capture the moment, boosting the potential to make a sale or sell additional items in an existing order. 

Detect fraudulent financial activities 

Seconds matter when it comes to detecting and mitigating financial fraud. Credit card fraud, identity theft, loan fraud, and other types of financial crimes can result in significant losses for banks and businesses, making low data latency an imperative. 

How Snowflake Optimizes Data Latency

Until now, reducing data latency in data applications and embedded analytics presented a unique challenge. Providing the speed and throughput these solutions required involved the use of an additional caching layer. This addition resulted in increased cost and architectural complexity. 

But with Snowflake’s elastic performance engine, businesses can optimize concurrency, throughput, and fast execution. Queries in Snowflake go through the following phases:

  • Setup: After a client application sends the query to Snowflake, Snowflake allocates initial resources.

  • Compilation: The query statement gets parsed and a plan is created to execute the query.

  • Scheduling: Snowflake checks if there are enough resources in the virtual warehouse to execute the query. If there are, the query transitions to the Execution phase. Otherwise, the query gets queued.

  • Execution: In this phase, Snowflake accesses data, joins tables, filters data by predicates, and performs aggregations. The results get returned to the requesting client application.

  • Querying: A query might spend some time in the queueing phase if there are no free resources to execute it, and move back to Scheduling for another attempt at execution. 

Plug into the Snowflake Data Cloud

Tap into the power of the Snowflake Data Cloud for your organization. Snowflake’s improved elastic performance engine offers industry-leading concurrency and throughput with faster execution. Easily discover and securely share live governed data across your business, with your customers, business partners, and any other organization that’s part of the Data Cloud. 

With Snowflake, you can democratize data analytics across your business so users at all levels and with varying expertise can make data-driven decisions. Create and run modern integrated data applications to best serve your customers, employees, and business partners, and develop new revenue streams based on data to help drive your business forward.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial