A step-by-step guide to building a data infrastructure with Snowflake

Step 1: Clarifying the purpose and requirements of the data infrastructure

What is Snowflake? — Its Strengths as a Data Platform

Snowflake is a platform specialized in data warehousing, data integration, and analysis on the cloud. Its greatest feature is its ability to store, process, and analyze data flexibly and efficiently. Unlike traditional database systems, Snowflake is designed to be scalable from the entire system, maintaining high performance even when multiple users and workloads are running simultaneously.

Additionally, Snowflake supports flexible data governance with data sharing capabilities, enabling seamless data exchange across companies and departments, maximizing the value of data and enabling real-time analytics that inform business decisions.

Current status and challenges of data utilization in companies

The biggest challenge facing companies in leveraging data is scattered data and inconsistencies between systems. Many companies collect data from various systems, such as ERP, CRM, and IoT devices, and this data is often not centralized. As a result, decision-making is delayed and companies are unable to take optimal actions using the data.

Furthermore, with the increasing volume and diversity of data held by companies today, traditional systems are no longer able to cope. To solve these issues, it is essential to build a "data infrastructure" that centrally manages data and utilizes it efficiently.

Why data integration is essential for building a data infrastructure

Designing data integration is essential for the success of a data infrastructure. Collecting data from different systems, integrating it, and managing it centrally is the first step for a company to utilize data. By properly designing data integration, data consistency between systems is maintained, creating a situation where data can be used quickly.

It is also important to streamline real-time data synchronization and conversion when data is stored in different formats. Optimizing this process will dramatically increase the speed of data analysis and enable data-driven decision-making across the enterprise.

Step 1: Clarifying the purpose and requirements of the data infrastructure

The first step in building a data infrastructure: setting goals and defining requirements

The first step in building a data infrastructure is to set goals and define requirements. Clarifying the company's objectives for using data will clarify what data is needed and how that data should be used. Data infrastructures are designed for a variety of purposes, including analysis, report creation, predictive analysis, and machine learning model training.

At this stage, business and technical stakeholders come together to share specific goals for data utilization and organize requirements based on those goals. For example, they listen to the detailed requests of each department, such as management wanting a real-time dashboard, the sales department wanting a customer analysis tool, and the manufacturing department wanting production data tracking, and then narrow down the overall goal.

Setting goals for data utilization

Setting goals for data utilization is the starting point for effectively promoting data utilization across the entire company. First, clarify the purpose of data utilization. For example, set specific goals such as accelerating decision-making, improving business efficiency, or strengthening marketing through customer analysis. Next, set KPIs (key performance indicators) to determine what results you want to achieve through data utilization, and create a system for quantitatively evaluating results.

It's important to avoid vague goal setting and to set specific, measurable goals. It's also important to create a plan that looks not only at short-term results, but also at the medium- to long-term effects of data utilization. Furthermore, the key to success is to consider in advance how to secure the resources, personnel, and budget needed to achieve your goals, and to proceed within a feasible scope.

Data infrastructure requirements definition

Requirements definition in data infrastructure construction is the process of clarifying the functions and performance that the system must fulfill based on the purpose of data utilization. At this stage, a wide range of factors are considered, including data handling and processing methods, system requirements, security, and user access management. The following elements are the main points to consider in requirements definition.

First, decide what data to collect and from which systems to integrate it. Then, clarify requirements such as data quality, accuracy, update frequency, and storage period, and formulate specific operational rules. Data security and governance requirements are also important. Define who can access which data, and how to manage it to maintain data integrity.

It is important to note that when defining requirements, it is necessary to fully understand the actual work flow on-site and the scenarios in which data will be used. To do this, it is important to reach a consensus among the parties involved, and it is necessary for everyone to have a common understanding. To ensure success, it is important to set requirements that are realistic and achievable, rather than being overly idealistic.

Step 2: Designing data sources and data integration

Identifying data sources and integrating needs

The first design step in building a data infrastructure is to identify the necessary data sources. There are a wide variety of data sources used within a company, and you need to organize the requirements for how they will be linked. It is important to understand what data will be extracted from different systems, such as ERP, CRM, IoT devices, social media, and even external databases, and the interrelationships between the data required between each system.

Data retention period definition

The length of time that data is retained depends on business needs and the purpose of the analysis. For example, if you are performing sales analysis or forecasting using past sales data, you may need several years' worth of data. Conversely, you may only need real-time data or data from the most recent few months. Clarify the specific analysis you will be performing and design your system so that it can retain the data required for that analysis. When considering how long to retain data, you need to estimate storage costs and balance costs with business needs. While retaining past data, you should also consider ways to compress and archive old data.

Data Freshness Definition

To keep data fresh, it is necessary to set the freshness according to the purpose of using the data. For example, data used for real-time analysis or dashboards needs to be kept up to date and requires frequent data updates. On the other hand, data used for historical data or trend analysis does not require frequent updates and may be sufficient with regular updates.

For data that needs to be updated in real time (for example, data from IoT devices or transaction history), the ETL (extract, transform, load) process should be performed in real time or near real time. On the other hand, for data that can be updated periodically using batch processing (for example, monthly reports or historical sales data), batch updates are used. It is also important to consider the cost of updates. Real-time data updates can incur high infrastructure and operational costs, so you should adjust your data processing method and update frequency as needed.

Step 3: Design your data pipeline

When building a data infrastructure with Snowflake, there are some differences in the data integration approaches for data lakes, data warehouses, and data marts. The characteristics of each data integration are explained below.

data integration with data lakes

One of the challenges many companies face when building a data infrastructure is integrating data from different sources. Data lakes store large amounts of structured and unstructured data, so they collect data from a variety of sources. A wide variety of data is generated within a company from different systems and applications. Integrating data collected from these multiple systems and departments into a form that can be used across the entire company while maintaining its consistency is extremely difficult and requires time and resources. For example, if customer data, sales data, inventory data, etc. are stored in different formats, a system is needed to centrally integrate this data. Therefore, when linking data sources to a data lake, it is desirable to use data integration tool that supports a wide range of data sources.

▼I want to know more about data lakes
⇒ Data Lake | Glossary

data integration with data warehouse

A data warehouse is a data storage location optimized for analysis and business intelligence (BI). Snowpipe is a feature that automatically ingests new data into Snowflake in real time when it is added to a data lake, such as Amazon S3 or Azure Blob Storage. This allows data to be reflected immediately, enabling rapid analysis. Snowpipe is also scalable and can handle increases in data volume, allowing for flexible operation in line with business growth. This has the major benefit of maintaining data freshness, maintaining data quality, and streamlining operations. For these reasons, Snowpipe is an efficient and high-performance solution for connecting a data lake to a data warehouse.

data integration with data mart

A data mart is a subset of data optimized for a specific business unit (e.g., sales, finance, marketing). Because data marts focus on a specific department or business use case, they typically extract data from a data warehouse. Therefore, the ELT process is used to data integration from a data warehouse into a data mart. While ETL (extract, transform, load) and ELT (extract, load, transform) are both widely used data integration processes, ELT involves first loading data into Snowflake and then performing transformations using SQL queries. This method allows for efficient processing of large amounts of data and allows transformations to be performed in real time, resulting in faster analytical results.

Best practices for automation and efficiency

Automating data pipelines directly leads to operational efficiency. By automating regular data transfers and synchronization, you can reduce the burden of manual work while maintaining data accuracy.

In addition, when operating a data pipeline, error detection and logging functions can be used to respond quickly when problems occur.

Step 4: Design and optimize your data model

Data Model Design in Snowflake

In Snowflake, proper schema and table design has a significant impact on data availability, performance, and manageability. Snowflake's ability to structure data multidimensionally allows you to build flexible and efficient data models.

For example, star and snowflake schemas facilitate BI and data analysis and optimize data warehouse performance. These schemas are designed to quickly retrieve the data needed for analysis and can handle complex queries.

Data model performance impacted by data integration

The design of data integration directly impacts the performance of your data model, so optimizing the integration method is crucial. In particular, when collecting data from different data sources, how that data is transformed and integrated will impact the final performance.

For example, when importing large amounts of data, you can improve data access speed by setting indexes and optimizing partitioning. Also, you need to design a data model that takes scalability into consideration so that performance does not decrease even as the amount of data increases.

Snowflake schema design and data integration tips

Consistent data integration and schema design is key in Snowflake. By leveraging Snowflake's powerful schema design capabilities, you can simplify data integration and effectively manage data from disparate sources.

For example, data from disparate data sources can be standardized and stored in a uniform format in Snowflake for efficient query processing, ensuring data accuracy and consistency, and enabling trustworthy analytical results.

Data quality and governance after data integration

Data quality and governance after data integration are essential to ensuring the reliability of data within a company. Snowflake provides tools to maintain high data quality. For example, you can maintain data quality by utilizing functions that automatically perform data validation, cleaning, and missing value handling.

Additionally, from a data governance perspective, access control and auditing capabilities allow you to monitor who has access to your data and how it is used, ensuring data security, transparency, and compliance.

Step 5: Security and Data Governance

Snowflake's Security Features and the Importance of Data Governance

Security and data governance are extremely important when designing a data infrastructure. Snowflake offers advanced security features to keep your company's data safe. For example, data encryption and access control can prevent unauthorized access from outside.

Snowflake also encrypts data while it is stored and in transit, minimizing the risk of data leaks to third parties. It also allows for detailed access rights to be set for each user, strengthening data access management.

Data Access Management and Data Masking

Data access management forms the foundation of an organization's information security. Snowflake employs role-based access control (RBAC), which allows you to set different access permissions for different users. This feature ensures that only necessary information is accessible and prevents unauthorized access.

Additionally, when handling personal or confidential information, data masking can be used to mask portions of the data accessed by users, maintaining confidentiality while operating, allowing companies to comply with legal and regulatory requirements.

Security best practices for data integration

Security is also a top priority when it comes to data integration. When transferring data, communications are encrypted to protect the data from malicious attacks. For example, by using data integration tool such as "HULFT Square," you can strengthen the security of transferred data by using TLS (Transport Layer Security) and VPN (Virtual Private Network) during data transfer.

Additionally, for security management purposes, it is necessary to obtain an audit log of data transfers and record who accessed what data and what operations were performed. This will enable early detection of unauthorized access or abnormal operations.

Step 6: Performance optimization and scaling

Snowflake Performance Optimization Techniques

When operating a data infrastructure, optimizing performance can be a challenge. Snowflake offers a variety of techniques to optimize query processing speed. For example, using Snowflake's clustering keys and index settings can speed up data access.

Furthermore, Snowflake automatically optimizes queries, enabling high-speed data analysis without requiring users to worry about complex optimization tasks. This is a major difference from traditional data warehouses.

Improved performance through data integration

Optimizing data integration processes can also improve overall performance. For example, by using HULFT Square, you can efficiently transfer and synchronize data, reducing unnecessary data duplication and excessive load.

Additionally, when real-time data synchronization is required, incremental data loading can be used to synchronize only the necessary data while maintaining performance, reducing the overall load on the system and enabling real-time analysis.

Collaboration strategies for scalability and data growth

Snowflake offers a scalable architecture that allows you to add resources as your data grows, allowing you to continue operating without losing performance even as your data volumes grow.

data integration also requires design with scalability in mind. Integration must be streamlined and automated to prevent system performance degradation when data volumes increase dramatically. By using HULFT Square, you can achieve scalable integration that can handle rapidly increasing amounts of data.

Step 7: Establishing a monitoring and operational system

Monitoring and maintenance of data infrastructure operations

When operating a data infrastructure, it is necessary to have a system in place to monitor whether data integration is proceeding smoothly and whether errors or delays are occurring. Snowflake provides a dashboard that allows you to monitor data usage and performance in real time, enabling you to detect abnormalities early on.

HULFT Square also has a function for monitoring data transfer and synchronization, and can notify you of errors or failed jobs in real time. This allows you to respond quickly if a problem occurs and minimize downtime.

Snowflake Monitoring and Management

Snowflake offers a wide range of monitoring features for efficient operational management. This allows you to optimize the performance of your data infrastructure and understand resource usage. Regular maintenance can be performed to improve performance and eliminate resource waste.

data integration monitoring and error detection

Monitoring data integration is an important step in early detection of abnormalities. With HULFT Square, you can identify errors and abnormalities that occur during data transfer in real time, creating a system that allows you to respond immediately. When you receive an error notification, you can quickly identify the problem and quickly restore the system.

Summary and next steps

data integration plays an important role in building a data infrastructure. Snowflake is a powerful platform for efficiently collecting, integrating, and analyzing data, and plays a central role in corporate data utilization. By utilizing data integration tool" HULFT Square," you can easily automate data integration, strengthen security, and optimize performance.

Future possibilities and prospects for data utilization

Data is becoming increasingly important these days, and by using a data warehouse like Snowflake, you can analyze and utilize even more data efficiently.

Furthermore, with the advancement of technologies such as AI, machine learning, and data science, the importance of data infrastructure will only increase, and data infrastructure will go beyond mere storage and be utilized as a strategic asset for companies.

The efficiency and automation of data integration using "HULFT Square" will accelerate the adoption of new technologies such as generative AI, enabling companies to make data-driven decisions quickly. In the future, the efficiency with which a company can operate its data infrastructure will have a major impact on its growth.

Learn from examples: Building a data infrastructure with Snowflake

So far, I have explained the steps for building a data infrastructure using Snowflake, but there are many parts that I was unable to explain in detail due to space constraints.
When attempting a new initiative, it is most efficient to learn from examples of companies that are actually putting it into practice.

Here, we introduce a case study in which our company, Saison Technology, built a data infrastructure using Snowflake with the aim of involving all employees in data utilization.

Conclusion

Building a data infrastructure is more than just a technical task; it is a critical element that supports a company's overall data strategy. Snowflake is a powerful platform that can meet a variety of business needs thanks to its scalability and flexibility. Furthermore, by utilizing data integration tool" HULFT Square," the process of data import, integration, and analysis becomes even smoother.

Now is the time to build a data infrastructure and take your company's data utilization to the next level. Use HULFT Square to more efficiently data integration with Snowflake and achieve competitive data utilization.

The person who wrote the article

Affiliation: Marketing Department

Seiji Hosomi

After working in systems development for around 10 years at a system integrator in Tokyo, he joined Appresso (now Saison Technology) in 2016. After working as a development engineer and then project manager for the data integration software DataSpider, he is currently in charge of data utilization in the marketing department. Drawing on the IT system utilization experience he gained during his time as a systems engineer, he supports customers in "data utilization" and "digital transformation."
(Affiliations are as of the time of publication)

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