For data engineering and analytics teams, real-time data processing can enable faster identification and correction of data errors, ensuring greater accuracy of data used for analysis. Real-time data processing ensures that only clean, validated data is available for analysis.
These advantages apply directly to analytics and business intelligence use cases, as they enable organizations to process and analyze data more quickly and accurately and respond more effectively to changing business needs.
A key risk worth noting for organizations south africa whatsapp number data pursuing real-time data capability is cost. Streaming rather than batch processing data may not generate additional storage or compute expense, but the engineering burden to cost-optimize streaming data pipelines and analytical models can be significant. Cloud computing costs can spiral, especially when storage and compute are concentrated within cloud data warehouses like Snowflake or Google BigQuery.
Addressing logging and semantic cataloging and mapping of streaming data early in the data pipeline can help reduce analytical expense downstream when it comes time to materialize, model and orchestrate data.
Getting Started with Real-Time Data
How does an organization adopt real-time data streaming? Many modern cloud services and retail data platforms already support streaming data transfer and processing. You can check with your current software and cloud service providers to confirm they support streaming data transfer.