Companies in the public and private sector are speeding up their digital transformation journey by adopting cloud-based technologies that will help them make informed decisions and scale operations. Cloud migration has become critical because of the expanding data collections most companies have, and they tend to run into issues such as storage, analytics and cost when attempting to make sense of these big data sets.

Traditional data infrastructure has proven incapable of meeting big data demands and hasn’t provided the value and growth opportunities required by companies. For most, big data is still captured in silos and the absence of a centralized system leaves room for errors because internal business units are not synchronized. These challenges in addition to the heavy cost of building a storage infrastructure have left many companies disadvantaged.

Fortunately, these challenges can easily be eliminated with a single storage and analytics platform. These cloud database management systems such as Oracle Database, Amazon Redshift, Database Lakehouse Platform, Microsoft Azure Data Lake, among others, exist to solve big data storage and analytics challenges for companies.

This article explores how Microsoft Azure Data Lake in particular, can help you structure your data and achieve your strategic goals.

Microsoft Azure Data Lake is a cloud platform designed as a repository for data of all sizes, formats and types. By removing complexities and increasing access to insights, it allows you to unlock value from all your unstructured, semi-structured and structured data as they are stored in a single, secure location.

Regardless of your company size, Azure Data Lake is a hub for all your data needs making it a generalized enterprise benefit that all your team members can access with only minimal training.

With Data Lake, you can also develop and run coordinated analytics across multiple platforms and languages (such as U-SQL, R, Python, and .Net). This unique capability helps professionals across skill sets gather insights to make quality business decisions and deliver maximum value across all areas of your business. In this feature lies the answer to many of the scalability challenges your company may be facing as it enables efficient performance, productivity and collaboration among teams.

Data Lake works with Azure Synapse Analytics, Power BI, Data Factory and other Azure technologies you may have previously invested in. You can easily connect to and process data with optimized data virtualization and no data movement. That said, team members with the traditional Microsoft or BI skill sets or training can easily get up-to-speed on using Data Lake within days.

With its pay-per-job model, the Data Lake system gives you flexibility to scale up or down based on your specific business needs. You only pay for what you use. The different storage types within the system come with different costs, so it is important to evaluate your data to determine what you use regularly or less. Also, it removes the cost of hiring specialized teams to run your data infrastructure as it is easy for existing employees to learn and adopt.

These are the solutions available to be explored and maximized on Azure Data Lake:

Although Hadoop hasn’t provided the value the world initially expected, Azure Data Lake is still compatible with the Hadoop Distributed File System (HDFS) allowing you to access and manage data on Data Lake Storage. On top of that, any existing HDFS tool you already use is compatible with Azure Data Lake Storage. You can also store data on the Blob storage which lets you store documents, HTML files, pictures as well as unlimited big data sets. It is flexible, extremely scalable, and comes at a low cost.

This is the solution that allows you to save cost as you only have to pay for the processing you use. Analytics is serverless, needs no virtual machines and easily processes petabytes of data for diverse workloads such as machine learning, image processing, querying, ETL, in seconds.

This is a data engineering tool that helps you to access, process and explore large scale data for batch and streaming workloads. Powered by Apache Spark, it allows seamless integration with opensource libraries and is compatible with languages such as R, SQL, and Python. Databricks enhances seamless collaboration and increased productivity among data engineers, data scientists and business analysts within your organization.

Azure Data Lake Services Generation 2 (ADLS Gen 2) is built on the Azure Blob Storage in consolidation with Azure Data Lake Services Generation 1, hence it affords tiered storage, low cost, and disaster recovery capabilities. It allows you to manage huge data volumes as it is designed to service multiple petabytes of information.

This end-to-end analytics service brings together big data analytics, data integration, enterprise data warehousing and data lake into a single and unified cloud workspace. It is built on the Azure Data Lake Storage (ADLS) Gen 2 and allows you to query both relational and non-relational data at a petabyte scale using your preferred programming language including T-SQL, KQL, Python, Scala, Spark SQL, and .Net.

This is a serverless NoSQL database for large scale, cloud-based data management. Because it is distributed on a global scale, Cosmos DB allows for speedy reads and multi-region writes from any location in the world. It guarantees 99.999 percent availability making performance delivery and workloads of any scale or size possible. It is a cost-effective option as it is fully managed, and pricing is based on consumption.

This is a fully managed SQL database that needs no updates or upgrades as it is always running on the latest version of the Microsoft SQL Server database engine. It allows you to operate on simple, flexible and responsive serverless compute options that automatically scale based on the demands of your workload.

The Microsoft Azure Data Lake offers you a data platform that is scalable, flexible, affordable and easy to manage. More importantly, you can now develop timely process that will amount to growth for your business and deliver optimal value to your customers.

Migrating your data from traditional infrastructure depends on its volume, variety and velocity. While the process may seem uncomplicated, it requires expertise and experience. Having the right partner to guide you through the world of Data Lake Storage and Analytics makes adoption, integration, and optimization seamless.