登录查看更多内容

What is the best way to retrieve data for real-time statistical analysis?

由人工智能和领英社区提供技术支持

Real-time statistical analysis is the process of applying statistical methods to data as it is generated or collected, without waiting for batch processing or offline analysis. Real-time statistical analysis can provide valuable insights, feedback, and alerts for various applications, such as web analytics, fraud detection, sensor networks, and streaming media. However, to perform real-time statistical analysis, you need to have a reliable and efficient way to retrieve data from different sources and formats. In this article, you will learn about some of the best practices and tools for data retrieval for real-time statistical analysis.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

Gorakh Wagh

Data Science Consultant | Top Machine Learning Voice | AI & Machine Learning Specialist | MCA | Product Leadership…

1 Data sources and formats

The first step to retrieve data for real-time statistical analysis is to identify the data sources and formats that you need to access and process. Data sources can be internal or external, structured or unstructured, static or dynamic, and so on. Data formats can be text, binary, JSON, XML, CSV, and so on. Depending on the data sources and formats, you may need different methods and tools to connect, extract, transform, and load (ETL) the data. For example, you may need to use APIs, web scraping, database queries, file systems, message queues, or streaming platforms to access the data.

添加您的观点

Gorakh Wagh

Data Science Consultant | Top Machine Learning Voice | AI & Machine Learning Specialist | MCA | Product Leadership Expert | Corporate Trainer | Computer Science SME | Data Engineering
举报内容
- Depending on the type of the data integration such as Propagation, consolidation or Federation we can decide the best way of retrieving data for the real time statistical analysis. - To retrieve the data many businesses follow the ETL Extract Transform and Load - But, the ETL has challenges as data latency, scaling issues and complexity. - The most preferred way to retrieve large amount of data more efficiently for real time statistical analysis is Extract Load Transform - ELT pipeline tools extract data from a source, load data directly into a target system like data warehouse, data lake and then transform it for analytics - While using ELT to avoid data governance problem we should compile it using some frameworks like HIPPA, GDPR

已翻译

赞

加载更多内容

2 Data quality and consistency

The second step to retrieve data for real-time statistical analysis is to ensure the data quality and consistency. Data quality refers to the accuracy, completeness, validity, and reliability of the data. Data consistency refers to the compatibility and alignment of the data across different sources and formats. To ensure data quality and consistency, you may need to perform various tasks, such as data cleaning, validation, normalization, standardization, deduplication, and integration. For example, you may need to remove or correct missing, invalid, or erroneous data, convert data to a common format or scale, merge or join data from different sources, and resolve any conflicts or discrepancies.

添加您的观点

3 Data storage and retrieval

The third step to retrieve data for real-time statistical analysis is to choose the appropriate data storage and retrieval system. Data storage and retrieval system is the software and hardware infrastructure that manages the storage, organization, access, and delivery of the data. Depending on the volume, velocity, variety, and veracity of the data, you may need different types of data storage and retrieval systems, such as relational databases, NoSQL databases, data warehouses, data lakes, or data streams. For example, you may need to use relational databases for structured and consistent data, NoSQL databases for unstructured and flexible data, data warehouses for historical and aggregated data, data lakes for raw and diverse data, or data streams for real-time and continuous data.

添加您的观点

4 Data analysis and visualization

The fourth step to retrieve data for real-time statistical analysis is to perform the data analysis and visualization. Data analysis is the process of applying statistical methods to the data to discover patterns, trends, relationships, and anomalies. Data visualization is the process of presenting the data analysis results in graphical or interactive forms, such as charts, graphs, maps, dashboards, or reports. Depending on the data analysis and visualization objectives, you may need different methods and tools to perform the tasks. For example, you may need to use descriptive, inferential, predictive, or prescriptive statistics, machine learning, or artificial intelligence to analyze the data, and use tools such as R, Python, Excel, Tableau, or Power BI to visualize the data.

添加您的观点

John Folkers

Analytics Architect
举报内容
Javascript continues to be disruptive in this space and is the basis for many tools including Power BI and D3.js. One easy way to combine real time data streams with javascript is to write a script which will restructure the raw data into samples of replayable sequential temporal json. Then just play the data out using D3.js. Another way is to use kafka messaging streams. Redpanda does this well.

已翻译

赞

5 Data security and privacy

The fifth step to retrieve data for real-time statistical analysis is to ensure the data security and privacy. Data security and privacy refer to the protection of the data from unauthorized access, use, modification, or disclosure. To ensure data security and privacy, you may need to follow various policies, regulations, and standards, such as GDPR, HIPAA, PCI DSS, or ISO 27001. You may also need to implement various measures, such as encryption, authentication, authorization, auditing, or backup, to safeguard the data. For example, you may need to encrypt the data in transit and at rest, use passwords, tokens, or certificates to authenticate the users and devices, use roles, permissions, or policies to authorize the data access and actions, use logs, alerts, or reports to audit the data activities, and use cloud, local, or hybrid storage to backup the data.

添加您的观点

6 Data optimization and scalability

The sixth and final step to retrieve data for real-time statistical analysis is to optimize and scale the data performance and capacity. Data optimization and scalability refer to the improvement and enhancement of the data speed, efficiency, quality, and reliability. To optimize and scale the data, you may need to use various techniques and tools, such as caching, indexing, partitioning, compression, or parallelization, to reduce the data latency, overhead, redundancy, or complexity. For example, you may need to use caching to store frequently accessed data in memory, indexing to speed up the data queries, partitioning to divide the data into smaller and manageable chunks, compression to reduce the data size and bandwidth, or parallelization to distribute the data processing across multiple cores or nodes.

添加您的观点

John Folkers

Analytics Architect
举报内容
Latency is king. I love these sorts of optimization problems... and typically the solution I prefer to use is parallelization. That is a heavyweight option because it can be expensive, but it will always scale out. Depending on simplicity, easier solutions are are indexing, partitioning or compression. Geographic distributed systems also help reduce latency. Strace is always helpful for identifying spinning threads which may indicate your storage is causing cpu to sit and wait. GDB with debug points is also helpful for this type of scenario. In the end, latency is a huge topic. SQL Server for example has hundreds and hundreds of wait types. It is always waiting on something. Usually, though, I use parallel systems to solve this.

已翻译

赞

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

John Folkers

Analytics Architect
举报内容
Real time streaming data always needs metadata for support functions. This is for status, state machine, and troubleshooting. For all of the above reasons, I wrote a custom logging module that outputs custom metadata and from there it's fairly easy to throw it up on a chart somewhere for engineering teams.

已翻译

赞

Statistics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What is the best way to retrieve data for real-time statistical analysis?

1

2

3

4

5

6

7

1 Data sources and formats

2 Data quality and consistency

3 Data storage and retrieval

4 Data analysis and visualization

5 Data security and privacy

6 Data optimization and scalability

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

更多Statistics相关文章

更多相关阅读内容

What is the best way to retrieve data for real-time statistical analysis?

1

2

3

4

5

6

7

1 Data sources and formats

2 Data quality and consistency

3 Data storage and retrieval

4 Data analysis and visualization

5 Data security and privacy

6 Data optimization and scalability

7 Here’s what else to consider

Statistics

给文章评分

感谢您的反馈

查看其他技能