What is The Right Latency for Data Analytics?
Daton doesn’t prefer replicating your data in real-time, but why? Because it is not productive for most of our users if you consider technical and cost factors. Let us see what should be the right latency for all your data replication jobs.
Real-time Data Replication
Daton replicates data to popular data warehouses like Google BigQuery, Amazon Redshift, Snowflake, and MySQL. Businesses use these data warehouses as the framework for effective data analytics. Data warehouses use columnar datastores to arrange data that analysts will access efficiently. This architectural design makes it easier to extract data for analysis, but at the same time, it becomes unsuitable for row-oriented updates in online transaction processing (OLTP).
The most productive way to load data into Amazon Redshift is with the COPY command. This command allocates the workload to the cluster nodes and also performs the load operations simultaneously. You get rows sorted and data distributed across node slices. Redshift documentation claims that one can add data to the tables using INSERT commands, but it is not as effective as COPY command. Instead, inserting a single row takes longer time than adding bulk records.
Real-time replication can downgrade the performance of a data warehouse. It delays the data loading process, using up processing resources which otherwise can be utilized in creating reports.
The Right Rate of Latency
Several Brands use Daton regularly, hence we have come to this conclusion that 15 to 20 minutes’ latency that the data sources provide is ideal for optimizing data warehouse performance and enhancing data analytics. Daton is optimized accordingly so that the users do not have to compromise with data warehouse performance and avoid unnecessary costs.
Powerful data analytics does not depend on fast data replication. It is always not true that real-time data will give you effective BI. The most important thing is how your organization is utilizing data analytics. The most important use is to provide the managers with relevant information for making better decisions. So, in this case, faster update of your BI dashboards will not make you the winner.
For most businesses and their use cases, a few-minutes old data is sufficiently updated data. How fast are your teams and systems making decisions? In most situation, it will be several minutes, or hours, decision making might also take days.
There are business requirements where near-real-time loading of replicated data will benefit. Let us take an example of an automated chatbot for attending customer’s inquiries. Here, it needs to know the context of the customer’s most recent interaction; hence the right latency will be a few seconds. Whereas, in the health sector, when you have to analyze data from a heart monitoring implant, more than a second’s latency is detrimental. Similarly, algorithmic trading in the stock markets requires microsecond-updated pricing information.
You might need real-time latency depending on what do you need to do with your data. To make the data replication process cost-effective, you need to understand the use case of a specific data-set. Data replication will be easily and effectively performed using cloud data pipelines.
Daton is an automated data pipeline which extracts from multiple sources for replicating data into data lakes or cloud data warehouses like Snowflake, Google Bigquery, Amazon Redshift where employees can use it for business intelligence and data analytics. It has flexible loading options which will allow you to optimize data replication by maximizing storage utilization and easy querying. Daton provides robust scheduling options and guarantees data consistency. The best part is that Daton is easy to set up even for those without any coding experience. It is the cheapest data pipeline available in the market.