Data Engineering

What is Right Latency for Data Analytics

6 minutes read

Modified on May 4, 2023

eCommerce

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.

Data Latency in Data Warehouse

Data latency is the time it takes for your data to become available in your database or data warehouse after an event occurs. Data latency can affect the quality and accuracy of your data analytics, as well as the performance of your data-driven applications. Therefore, it is important to measure and optimize data latency in your data warehouse.

One way to measure data latency in your data warehouse is to compare the timestamps of the events with the timestamps of the corresponding records in the data warehouse. This can give you an idea of how long it takes for your data pipeline to collect, transform, and load the data from various sources into your data warehouse. However, this method may not account for late-arriving data, which can cause discrepancies and inconsistencies in your data warehouse.

Another way to measure data latency in your data warehouse is to use a dedicated tool or service that monitors and reports on your data pipeline performance. For example, Snowplow provides a dashboard that shows the average and maximum latency of your data ingestion, as well as the distribution of latency across different stages of your data pipeline. This can help you identify and troubleshoot any bottlenecks or issues that may cause high data latency in your data warehouse.

Latency Data Collection

Latency data collection refers to the process of capturing and storing the latency metrics of your data pipeline. Latency data collection can help you analyze and optimize your data pipeline performance, as well as diagnose and resolve any problems that may affect your data quality and availability.

There are different methods and tools for latency data collection, depending on the type and source of your data. For example, if you are collecting log data from Azure resources, you can use Azure Monitor to track and report the ingestion time of your log data. Azure Monitor also provides alerts and notifications for any abnormal or unexpected changes in your log data ingestion time.

If you are collecting event data from web or mobile applications, you can use Adobe Analytics to measure and report on the latency of your data collection servers. Adobe Analytics also provides information on how latency affects your report suite processing and availability.

If you are collecting sensor data from battery-free wireless sensor networks (BF-WSNs), you can use latency-efficient data collection scheduling algorithms to minimize the latency of your data collection. These algorithms take into account the energy harvesting and communication constraints of BF-WSNs, as well as the network topology and traffic patterns.

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 the COPY command. Instead, inserting a single row takes a 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 that otherwise can be utilized in creating reports.

Right Latency for Data Analytics

Several Brands use Daton regularly, hence we have come to the conclusion that the 15 to 20 minutes of 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 updates of your BI dashboards will not make you the winner.

For most businesses and use cases, a few minutes of old data is sufficiently updated. How fast are your teams and systems making decisions? In most situations, 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 to customers’ 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, more than a second’s latency is detrimental when you have to analyze data from a heart monitoring implant. Similarly, algorithmic trading in the stock markets requires microsecond-updated pricing information.

You might need right real-time latency depending on what 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 that extracts from multiple sources for replicating data into data lakes or cloud data warehouses like Snowflake, Google Bigquery, and 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. Sign up for a free trial of Daton today.

A critical part of business insight is information examination. It involves analyzing a large amount of data to identify patterns, make predictions, and aid decision-making. However, the efficiency of data analytics depends on how quickly data is processed and analyzed. This speed is measured by latency, the time it takes for data to be transmitted and processed. It is concluded that the 15 to 20 minutes of latency provided by the data sources is ideal for improving data analytics and data warehouse performance because Daton is used by many brands frequently. Daton is upgraded as such to guarantee that clients don't need to forfeit information distribution center execution and that clients don't bring about extra expenses.

The need for faster data delivery grows in importance as businesses strive to develop data products that are more advanced. Instances of purpose cases that advantage from quicker accessibility of information include: Optimize the front page of a news site with breaking news stories to balance supply and demand in a two-sided market. Retarget customers who have abandoned their cart or interacted with an advertisement in real-time. Make product or content recommendations based on users' most recent actions. Detect fraudulent or suspicious behavior in real-time. Since quicker information handling can yield genuine business esteem, every one of these utilization cases gains from quicker information accessibility. Additionally, the quicker a bank can identify potentially fraudulent transactions, the lower the cost of containing instances of extortion.

Idleness can affect information examination execution in more than one way. Right off the bat, high idleness can create setbacks for information transmission, which can dial back the general information handling and examination. As a result, decision-making may take longer, and productivity may suffer. Also, high inactivity can influence the exactness of information investigation, making information obsolete when handled. This can lead to poor decision-making and inaccurate results. Last but not least, high latency can also affect data storage because it can cause backups of data, which can reduce storage capacity.

Perceiving how late a significant portion of the information in your data set or information stockroom is is one method for estimating and reporting on your information dormancy. Regardless, this gives an incredibly best guess since it will likely not think about the late appearance of data. Alternatively, you can examine the processing progress of a stream processed by a microservice; notwithstanding, this approach is hard to convert into a period estimation that is easy to understand and investigate. On the other hand, the time it takes for the information to be handled and kept in touch with the capacity focus that is being referred to would be the most precise measurement. A company can accurately report on latency, see how quickly behavioral data loads into a database or warehouse, and be more transparent about the accuracy of its data products if it has access to this data.

Several factors can make achieving the best data analytics latency challenging. Right off the bat, there might be restrictions on accessible transmission capacity, which can restrict information transmission paces and increment inactivity. Furthermore, there might be constraints on accessible information handling capacities, which can influence idleness and information investigation execution. Last but not least, the capacity for data processing and analysis may need to be improved by storage restrictions. A comprehensive strategy that includes investing in the appropriate infrastructure and technologies, optimizing data processing algorithms, and implementing effective data storage solutions is necessary to overcome these obstacles.