Data used in bioinformatics is often large, messy, and in esoteric file formats. Luckily for us, cloud-based data lakes are the perfect choice for storing and organizing data at-scale. Plus, having your data available in your cloud environment unlocks its use with other cloud tools for scalable bioinformatics pipelines, machine learning, reporting, and more! As a first step when you begin working with us, we'll help you determine the best organization strategy for the data in your genomics data lake. Organizing your data is key to making it useful as your lake continues to grow and eventually houses all sorts of data.

Features:

• Petabyte-Scale on the Cheap: Whether you have 10 samples or 10 million, the lake can handle it. Plus, it's a very cost-efficient service, even at high volumes of data.
• Secure and Safe: Lock down particular folders or files using Azure Active Directory. Use geo-redundancy to protect your data for ever getting lost.
• Snapshots and Lifecycles Capture: Previous versions of ever-changing data. Auto-demote unused data based on activity.
• Well-Connected: Connect to your data lake from cloud tools such as Azure Machine Learning, Databricks, Synapse, and Snowflake.

The Goal: Scalable Queries

Once your data is organized in a data lake, you can then use tools like Azure Databricks or Azure Synapse Analytics to query across your entire data estate, unlocking insights faster than ever.

• Ask Questions Across Your Studies: "How many of the samples in Study B have Gene ERBB2 expression > 10 TPM across all RNA-seq analyses?"
• Quickly Aggregate for Faster Reporting: "Return a combined list of all spectrophotometer readings for Client Q's studies in 2022."
• Analyze with Ease: "What is the average expression Protein Q9NZQ7 across all of Client X's spatial proteomics runs?"