APC 技術ブログ


株式会社 エーピーコミュニケーションズの技術ブログです。

Data Quality: Fast and Slow


I'm Sasaki from the Global Engineering Department of the GLB Division. I wrote an article summarizing the contents of the session based on the report by Mr. Nagae, who is participating in Data + AI SUMMIT2023 (DAIS) on site.

Articles about the session at DAIS are summarized on the special site below.


Importance of real-time data processing and timely architecture implementation

​ Today, I would like to talk to everyone interested in data & AI about the importance of real-time data processing and timely architecture implementation. This article is based on the content of the lecture "Data Quality: Fast and Slow" and will be explained in an easy-to-understand manner for Japanese readers.


​ First, I would like to introduce the title of the talk, "Data Quality: Fast and Slow." In this talk, Dylan, who has worked at Databricks for over five years, discusses the importance of real-time data processing and the need to implement well-timed architectures. The intended target audience is technologists interested in data & AI, business leaders interested in improving data processing, and real-time data processing professionals. ​ Let's take a look at the importance of real-time data processing. ​

Importance of real-time data processing

​ In recent years, real-time technology has become increasingly important in modern data warehouses. The reason is that real-time data plays a big role in business, such as streaming services like Netflix and TikTok and real-time bidding for advertising. ​ Real-time technology is used in many industries. Typical examples include the following. ​

  1. Streaming services (Netflix, TikTok, etc.)
  2. Real-time bidding for ads
  3. Faster financial transactions
  4. Social Media Timeline Updates

​ In these cases, real-time data processing contributes to business growth and competitiveness. ​

Architecture implementation at the right time

​ Implementing a well-timed architecture is critical to achieving real-time data processing. Specifically, the following factors should be considered: ​

  1. Speed ​​of data collection, processing and analysis
  2. Accuracy and reliability of data
  3. System scalability and extensibility
  4. Cost optimization

​ Real-time data processing can be effectively utilized by building an architecture that incorporates these elements in a well-balanced manner. ​

Utilization of the latest concepts, functions and services

In order to realize real-time data processing, it is necessary to utilize the latest concepts, functions, and services. For example, technologies such as: ​

  1. Stream processing engine (Apache Kafka, Apache Flink, etc.)
  2. Data Warehouse (Amazon Redshift, Google BigQuery, etc.)
  3. Data analysis tools (Tableau, Looker, etc.)

​ The combination of these technologies effectively realizes real-time data processing. ​


​ Real-time data processing has become a key component of modern data warehouses. Implementing the right architecture at the right time and taking advantage of the latest concepts, features and services can help your business grow and become more competitive. The importance of real-time data processing is expected to increase in the future, so let's keep an eye on its trends.

​## Conclusion This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.


Thank you for your continued support!

Translated by Johann