Enterprise Data Warehouse

---

Mazzy Technologies can bring together all of you data, no matter the scale or format.  Additionally, this solution provides a way for all users to gain insights from your data though analytical dashboards, operations reports and advanced analytics. 

Reliable data, especially when aggregated over time, helps users make smarter, more informed decisions about the way they run their organization—and data warehouses are what makes that possible. The benefits of enterprise data warehousing are myriad, but some of the most impactful advantages include:

• Consolidating data from multiple sources into one single source of truth
• Storing and analyzing long-term historical data spanning months and years
• Cleansing and transforming data so that it is accurate, consistent, and standardized in structure and form
• Reducing query times when gathering data and processing analytics, which improves overall performance across systems
• Efficiently loading data without having to deal with the costs of deployment or infrastructure
• Securing data so that it is private, protected, and safe
• Preparing data for analysis through data mining, visualization tools, and other forms of advanced analytics

Now that you know why and when you should use a data warehouse, let's dive into how one works by looking at data warehouse design. A data warehouse is more than just a single silo operating on its own. Rather, it is a highly structured, carefully architected system composed of multiple tiers that interact with your data—and each other—in different ways. Typically, these tiers include:

The bottom tier: Data is ingested from multiple sources, then cleansed and transformed for other applications to use in a process called extract, transform, and load (ETL). The bottom tier is also where data is stored and optimized, which leads to faster query times and better performance overall.

Middle tier: This is where you'll find the analytics engine, also known as the online analytical processing (OLAP) server. OLAP servers access large volumes of data from the data warehouse at a high speed, which leads to lightning-fast results.

Top tier: The top tier is where the front-end interface visually presents the processed data, which analysts may access and use for all their reporting and self-service BI needs.

When designing and building a data warehouse, it's important to consider the goals of your organization, both long-term and ad-hoc, as well as the nature of your data. How many data sources are you integrating? Do you plan on automating your workflows? How will you explore and analyze your data? Your build-out will vary depending on the complexity of your needs, but a typical enterprise database warehouse may consist of the following components:

• Data sources that extract operational data from point-of-sale systems, business applications, and other relational databases
• A staging area where data is cleaned and transformed for the warehouse or centralized repository
• A warehouse or centralized repository which stores processed operational data, metadata, summary data, and raw data for easy user access
• The addition of data marts, which takes data from the centralized repository and serves it in subsets to selected groups of users
• A sandbox, which data scientists may use to test new forms of data exploration in a protected environment
• A wide variety of data warehousing tools, frameworks, and APIs for integration, storage, performance, and analysis

Cloud and hybrid cloud data warehousing: A unified, cloud-based data warehousing solution, such as Azure Synapse Analytics, gives organizations the ability to scale, compute, and store at a faster speed and lower cost.

Data integration tools: ETL pipelines enable users to create, schedule, and orchestrate their workflows so that source data is automatically integrated, cleansed, and standardized.

Object storage: An object storage solution can hold large amounts of structured, semi-structured, and unstructured data, which makes it perfect for staging source data before it's loaded into the warehouse.

Warehousing tools: A distributed storage solution holds large sets of data in relational tables with columnar storage. This greatly lowers costs, improves query performance, and speeds up time to insight.

Performance tools: To boost the performance of your applications, you may want to incorporate Apache Spark, an open-source parallel processing framework that supports in-memory processing.

Resource and workload management: A resource manager allocates computing power to your workloads so that you may load, analyze, manage, and export data accordingly.

Data modeling: Data modeling combines multiple data sources into a single semantic model, providing a structured, streamlined view of your data.

Business intelligence tools: Business analytics tools help deliver insights to users in the form of dashboards, reports, and other visualization tools.

Security and privacy features: Security and compliance features like data encryption, user authentication, and access monitoring ensure that your data stays protected.