Data Lake Architecture (Azure)
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured/raw data.
Unlike a hierarchal Data Warehouse, a Data Lake has a flat architecture.
We design your Data Lake Environment to fit your specific and unique business needs and objectives using a mix of Azure technologies.
Data Ingestion (Azure)
Information is processed into a Data Lake through what we call a "Information Pipeline".
The Information Pipeline begins with data ingestion, but also needs to take into consideration monitoring, coordination, and validations; depending on the types and class of information being consumed.
Data is processed through the Information Pipeline at a variety of intervals depending on business needs and objectives.
Some types of data needs to be consumed and curated in real-time.
Other types of data may only need to be processed through the "Information Pipeline" weekly, monthly, or annually.
Additionally, how data is accessed varies greatly.
Some information is available via API, other information from remote sensing equipment, some within SQL data stores, and yet other needs to be consumed from an FTP endpoint.
Depending on the source of the data will influence the method that is used to extract that information.
Whether your information is coming from field sensors, IoT devices, weather stations, or batch processing environments, our event centric approach can handle it.
We love the phrase "Begin with the end in mind". It is particularly relevant from a systems operational perspective.
An important part of operating a Data Lake is understanding how all of the components that comprise the Data Lake Environment are operating and performing.
The ability to generate notifications, and potentially create support/trouble tickets, when issues occur or operational performance falls below predefined thresholds is important to realizing the full potential of your Data Lake Environment.
Monitoring is also a critical part of ensuring that Service Level Agreements (SLAs) are met.
Data Lake Governance is used to ensure that all parties adhere to the Rules, Rights and Processes for the use and management of the Data Lake.
This means that the governance approach will identify and define the standards and templates needed to ensure the consistency, efficiency, and effectiveness of the interactions.
Governance is ultimately the final authority for negotiating the relationships, duties, rights, obligations and privileges of all parties.
Data Lake "Utopia"
The implications of data within an organization continues to evolve at a rapid pace and the advent of the "Data Lake" has impacted that positively.
The technology stack associated with Enterprise Data Lakes promises a lot more.
While technologists and business professionals continue to debate on approaches and security, Data Lakes is making its way into the realm of an essential business tool.
Enterprises must quickly change gears and incorporate Enterprise Data Lake technologies to deliver the classes of services internal user and clients are starting to expect as a norm.
The positive impacts of Enterprise Data Lake technology is nothing less that astonishing.
ExcelliMatrix follows a Six-Element iterative (Scrum/Agile) approach to implementing your Big Data solution.
Our approach decreases time-to-value by focusing on a prioritized agenda of data needs and results.