{"id":518226,"date":"2024-11-12T10:33:03","date_gmt":"2024-11-12T10:33:03","guid":{"rendered":"https:\/\/www.devoteam.com\/expert-view\/real-time-data-necessity\/"},"modified":"2024-11-12T10:33:03","modified_gmt":"2024-11-12T10:33:03","slug":"real-time-data-necessity","status":"publish","type":"expert-view","link":"https:\/\/devoteam.info\/be\/expert-view\/real-time-data-necessity\/","title":{"rendered":"Real-Time Data, a Luxury or Necessity?"},"content":{"rendered":"\n

“We’ve been looking for real-time data for a long time. But now, we can’t do without it!” Let’s try to convince the last few who are still reluctant. Two recent observations highlight a growing trend towards real-time data<\/strong>.<\/p>\n\n\n\n

Evolving Data Requirements<\/h3>\n\n\n\n

Firstly, expectations for real-time data have grown significantly, mirroring the rapid changes in corporate strategies. However, while some IT teams may still claim they don’t need real-time data, the reality is that waiting until the next morning to review the previous day’s performance is no longer acceptable.<\/p>\n\n\n\n

Technological Capabilities<\/h3>\n\n\n\n

Cloud data platforms offer innovative solutions to meet the increasing demand for real-time data. As a result, real-time analytics has become strategically important and readily accessible, making it a key factor for businesses today. New data platforms<\/a> have revolutionised how companies manage and interpret their information.<\/p>\n\n\n\n

What is Real-Time Data?<\/h3>\n\n\n\n

Real-time data captures the current moment, providing immediate insights. In contrast, unlike traditional batch processing, real-time data aligns business needs with data availability. It empowers businesses to understand, predict, and react instantly.<\/p>\n\n\n\n

Real-time data means having access to data precisely when the business requires it. While immediate access to every new order may not be necessary, waiting an hour to streamline operations is inefficient. A few minutes is usually sufficient for operational needs. Similarly, daily or weekly customer segmentation updates are often more effective than traditional monthly or annual updates.<\/p>\n\n\n\n

Essentially, real-time data eliminates technological constraints on data availability.<\/p>\n\n\n\n

Implementing Real-Time Data & Data Ingestion<\/h3>\n\n\n\n

Adopting real-time data is a gradual process. Start with micro-files on cloud storage (S3, Blob, GCS, etc.). Modern data platforms allow for data ingestion as it becomes available. Data sync solutions can also synchronise operational databases (using tools like Fivetran<\/a>, Airbyte<\/a>, or SNP Glue). Regular ingestion (every 5-10 minutes) can create a near real-time experience for business teams. Simple solutions are often more effective than complex ones, so Kafka for streaming isn’t always necessary.<\/p>\n\n\n\n

Data Transformation<\/h3>\n\n\n\n

Advanced functionalities in platforms like Snowflake<\/a> (Dynamic Tables) and Databricks<\/a> (Live Tables) simplify real-time data updates. Incremental updates are possible with a single SQL query (CTAS).<\/p>\n\n\n\n

In contrast, traditional ETL and ELT products often struggle to keep pace with these capabilities. However, it’s still possible to orchestrate manual update pipelines using dbt and incremental update templates.<\/p>\n\n\n\n

Real-Time Data: The Foundation of an Active Data Lake<\/h3>\n\n\n\n

Data lakes are evolving from static repositories to active data sources for various business functions, including operational systems, marketing, and vendor applications. Therefore, this shift requires up-to-date data, making real-time data management essential.<\/p>\n\n\n\n

Monitoring and Data Quality<\/h3>\n\n\n\n

Real-time data ingestion increases the risk of undetected data quality issues. As a result, data observability<\/a> becomes crucial, encompassing:<\/p>\n\n\n\n