Today’s economy is transactional, and data is the currency that drives it. Especially in the cloud, increasingly more data is collected, analyzed, distributed, and stored in order for organizations to make smart decisions, move projects forward, and serve the needs of their customers and stakeholders.
Much of that data is, in essence, waiting around until it’s usable. Organizations usually store it in a non-structured format called a data lake. It’s an effective way to store data in the cloud, but not always efficient. Fortunately, Microsoft has a solution called Azure Data Lake that enables enterprises to store, process, and make efficient use of data and give it context. It helps organizations get the most out of their data investments and delivers major advantages in terms of speed, scale, and business value.
The term “big data” has become so common that the reality of what it represents can easily get lost. There is perhaps no aspect to modern IT environments that is more important, but that also requires so much management complexity. Consider these statistics:
- 2.5 quintillion bytes (2.3 trillion gigabytes) of data are created every day 1
- 40 zettabytes (43 trillion gigabytes) of data will be created by 2020 2
- Most companies in the US have at least 100 terabytes (100,000 gigabytes) of stored data 3
- 90 percent of the data available across the globe was created in the last two years 4
In earlier iterations of IT infrastructures, we relied on data warehousing and databases to keep data close and available to be called up when needed. When data could be organized in highly governed environments where change was irregular, this system was adequate. The cloud changed all that because, by nature, it is ephemeral and continuously changing. Users can contribute and change data, and with APIs and easy-to-use integration strategies, data is shaped and shifted to meet the demands of both internal and external users.
Microsoft, as one of the earliest to recognize the potential for the cloud, developed Azure as a way to improve communication and collaboration among users, and to give customers major organizational and cost advantages through an innovative, continuously updated platform. With their legacy as the first true democratizer of enterprise knowledge management, they identified ways to use technology on top of Azure to get data in front of users and decision makers.
Azure Data Lake was created to address the growing desire by enterprises to consume and deliver data. It uses advanced data visualization and innovative analytics capabilities to give greater meaning. For developers, that means the ability to integrate data with applications in a contextually relevant way. For business users, it is a way to shed meaningful perspectives and derive business insights that help inform decision making.
Cloud users have a lot of options when it comes to data management, but managing data alone isn’t the real issue. Ultimately, modern enterprises need a solution like Azure Data Lake because it was developed specifically for use with a public cloud infrastructure. It also complements data-related issues with an emphasis on both scale and speed. Cloud users should consider how it is able to work in concert with Azure and the advantages it demonstrates:
- Runs on Hadoop: Azure Data Lake is structured and organized on the Hadoop File System. This is an important differentiator for Azure users because Hadoop employs an advanced file organizing approach based on data locality, rather than parallel file systems. Microsoft uses something called HDInsight, which is a managed Hadoop offering that delivers open source analytic clusters for Spark, Hive, Map Reduce, HBase, Storm, Kafka, and R-Server. This gives Azure Data Lake broad and wide usability across open source platforms and tools, and it offers organizations the flexibility to apply a consistent method of big data management in multiplatform environments.
- Optimized for parallel processing: Hadoop is built to keep processing close to data on the compute layer of the cloud; as a result, Azure Data Lake is able to support the execution and delivery of massive queries at speed. Queries can be simple or complex, but the native processing style of Azure Data Lake creates the ability to pull data real-time from data stored within Azure.
- Comprehensive security: Cloud security adheres to a shared responsibility model, so Azure users must maintain the security of their data as it is stored and transacted in the cloud. Data Lake adheres to these principles and provides both single sign-on and multi-factor authentication as critical user security measures. Additionally, at the ID layer, Data Lake works seamlessly with Azure Active Directory to support user, group, and ACL access.
- Native analytics: Azure Data Lake comes with built-in capabilities to execute on-demand analysis of data, at scale. This is done as an SaaS offering, and is natively part of Azure Data Lake. Being integrated into the product is critical because it gives developers better ability to perform custom reporting and analysis. Most infrastructure analytics jobs require the work of third-party tools, but Azure Data Lake can easily identify data and perform analysis across petabytes of data using .NET-friendly U-SQL.
- Built for cloud environments: The flexibility of the cloud means that there are almost unlimited data sources, and new ones are constantly being spun up in an ad-hoc fashion in order to meet changing business needs. Smart organizations want to know how all their data is being used, where it’s being transacted, and how they can optimize delivery of it. Azure Data Lake has built-in capabilities so developers can run parallel data transformation and processing operations in .Net, Python, and other frameworks. That enables them to pull data from various sources and put it to use in the most usable fashion.
- Scalable storage: Data Lake storage is handled with a file system that scales on-demand for data at rest and for workloads that reside in Azure Blob Storage. Because it has native integration capabilities with other Azure services like Databricks and Data Factory, Data Lake storage is a single source for all Azure-related data needs.
Some enterprises view data as a problem to be managed. When relying on multiple storage, analytics, and transactional systems, data management can certainly be onerous. Data Lake offers a different path; it’s not really an integrated solution as it is a single, end-to-end solution that works across the entire Azure surface to ensure organizations can optimize their data for both technology and business needs.