Get More from Your Data with the Modern Data Stack
Capable of handling today’s big data workloads, the modern data stack enables organizations to use data to its fullest potential. With automated data integration, robust compute and storage, and powerful analytics tools, businesses are using the modern data stack to make better decisions, find new ways to improve operational efficiency, and increase profitability.
What Is a Modern Data Stack?
As the name implies, the modern data stack is a collection of cloud-based tools that function together in a layer-type structure. The first is the data ingestion layer. The modern data stack uses automated data integration technologies to transport data from a variety of sources, including in-house databases, IoT devices, server logs, third-party data, and more. This data is collected into the second layer, a centralized platform such as a cloud data warehouse or a data lake. Once there, the data can be transformed and worked with using a variety of tools such as analytics software.
Why Migrate to a Modern Data Stack?
Companies still using a traditional data stack are at a distinct disadvantage since legacy systems just can’t match the needs of data-driven organizations. Here are five reasons a modern data stack is worth the investment.
1. Highly scalable
Many of today’s data applications involve massive volumes of data, from predictive analytics tools to machine learning applications. The storage and compute requirements can quickly outpace the capabilities of on-premises systems. In contrast, modern data stacks are cloud-native, with elastic compute power that can scale up or down on demand. The cloud unlocks near-infinite storage at costs far lower than on-premises solutions. A modern data stack can grow organically with the business, avoiding the costly infrastructure bottlenecks that plague legacy solutions.
2. Reduces IT resource requirements
Relying on physical hardware places the burden of IT maintenance, routine updates, capacity upgrades, etc., directly on the business. The modern data stack removes that burden since cloud providers manage the infrastructure. When using a modern data stack, organizations can focus their resources on putting their data to use.
3. Improves data governance and security
The modern data stack inherently makes governance, privacy controls, and data security simpler to manage. A cloud data platform can help businesses more easily comply with industry and government-mandated data security standards.
4. Supports data-driven decision-making
The modern data stack enables faster iteration and reduces time to insight. Freed from the time-consuming process of manually creating and maintaining data pipelines and ETL systems, companies can access fresh data from user-friendly analytics dashboards to unlock time-sensitive business opportunities.
5. Speeds data science projects
Data science projects such as machine learning applications exponentially benefit from the modern data stack. Using a platform with robust modern capabilities, data scientists can bring nearly all types of data into their models, scale processing, take advantage of open-source libraries and automation, and seamlessly connect machine learning tools.
Capabilities of the Modern Data Stack
One of the primary benefits of the modern data stack is its versatility. Businesses can customize their data stack to meet their unique requirements, updating and adding new components, capacity, and functionality when needed.
Analytics and business intelligence
Capturing data from first-, second-, and third-party sources is essential for solving many business problems and recognizing time-sensitive opportunities to increase sales, provide a better customer experience, improve efficiency, or boost productivity. Data analytics tools provide decision-makers with clear visibility, a deeper understanding of the business question at hand, and insights for taking action.
Integration and modeling
Before it can be used to generate actionable insights and power data applications, data must first be cleaned, organized, and made accessible. Data transformation changes the format, structure, or values of data to make it usable to both humans and computers. The data integration process also involves centralizing data.
Data science
Artificial intelligence (AI) and machine learning (ML) allow businesses to work efficiently with the massive pools of data accessible today. The modern data stack provides data scientists with the tools and capabilities needed to build and deploy models used for accelerating predictive analytics, automated decision engines, and more.
Building a Modern Data Stack with Snowflake
The Snowflake Data Cloud is at the core of the modern data stack. With intelligent infrastructure, an elastic performance engine, and optimized storage, the Data Cloud is an ideal choice for extracting the most value from your data. With Snowflake, you benefit from a fully managed platform that capitalizes on the near-infinite resources of the cloud.
Leverage Snowflake’s speed, concurrency, and extensibility to develop and run data pipelines, models, and applications. Enable global, cross-cloud data sharing and collaboration. Work and collaborate seamlessly across multiple clouds no matter where data, applications, or your local and global business communities reside.
Download our guide, 6 Paths to a Modern Data Stack, to learn more about building a modern data stack with Snowflake.