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Big Data Management

Big Data management is the handling and oversight of large volumes of structured and unstructured data across an organization.

According to the International Data Corporation (IDC), revenues for big data and business analytics solutions will exceed $189 billion in 2019, with double-digit growth expected through 2022.

With Big Data management's tremendous growth, it's worthwhile to examine what defines Big Data concept and exactly how big is Big Data.



Big Data describes the large volume of data (structured and unstructured) that is too big to be housed or processed with a single computer or traditional computing methods.

Big Data takes on a variety of challenges with the objective of delivering insights into data that would be undetectable through singular computing methods. The sheer volume of the data, along with its velocity and variety comprise the basic parameters of Big Data.

The size of the data itself is commonly measured in petabytes (1,024 terabyes or one million gigabytes) and exabytes (1,024 petabytes), volumes that exceed the compute abilities of a single computer. But size isn't the only factor in the Big Data concept.

Big Data management includes data storage and data warehousing, typically within a cloud-based architecture. The cloud-based infrastructure enables data scientists, data analysts and engineers to keep pace with the speed of information while maintaining the integrity of data, which is often from multiple sources in multiple forms.


Big Data relies upon countless data points and leverages analytics applications to reveal thorough and complete insights to the data.

It helps organizations reduce costs through greater efficiency, uncover trends that lead to more informed decision making and innovate based on credible real-time feedback.

Big Data is relied on more and more today in retail, government planning, life sciences, healthcare and banking. It's applied to improve the customer experience, product development, diagnostics, fraud detection, compliance and several other initiatives.


Snowflake's easy-to-use cloud data platform with data warehouse as a service (DWaaS) and cloud data lake provides a cloud-based single solution to Big Data management needs. Snowflake is the strongest of the Hadoop alternatives in big data management.

Through its partnership with cloud Big Data as-a-service-company Qubole, Snowflake maximizes its data warehouse potential. The partnership enables customers to use Apache Spark in Qubole with data stored in Snowflake.

The integration of the two products increases the capabilities for building machine learning (ML) and artificial intelligence (AI) models in Apache Spark using data stored in Snowflake.

Additionally, Snowflake's continuous ingestion service, Snowpipe, provides an avenue for pulling data into an existing Snowflake database and streaming near real-time data through Oracle GoldenGate. Big Data with Oracle works hand-in-hand with Snowflake.


Global enthusiasm for Big Data has spawned new opportunities for data warehousing providers.

Snowflake's unique cloud-based architecture offers the security, insights and scalability for any business looking to leverage its data.

Virtual Hands-on Lab

This hands-on workshop focuses on increasing your efficiency, scaling to your needs and analyzing your data thoroughly. Learn how to set up a data warehouse and generate the insights your business needs.

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Learn how Snowflake has revolutionized cloud data warehousing and cloud analytics. Our weekly 30-minute demos feature product experts introducing key Snowflake features, explaining Snowflake's unique architecture and answering live questions.