Azure Data Lake | Starschema
Gradient starschema cut 02

Azure Data Lake

Full-stack data lake implementation

Ic services data engineering
Data Engineering Challenges
  • Large numbers of disparate data sources
  • Huge volume of structured and unstructured data
  • Very volatile data velocity
Ic services strategic consulting
Operational Challenges
  • Difficult to implement
  • Lack of specialized skills
  • Expensive operational costs
  • Lack of integration between ETL, Store, Analyze and Reporting
Ic services data science
Data Consumption Challenges
  • Lack of sophisticated processing and automation of data collected
  • Slow or poor quality data presented to users
  • Difficult to use reports


  • The flexibility of the Datalake architecture supports other Azure components, creating a custom designed architecture suited to your company’s needs.
  • HDInsights along with Event Hub store semi/non-structured data, and process them with Datakbricks jobs such as Spark or R.
  • Microsoft Azure Datawarehouse, capable of storing and processing hundreds of terrabytes of data, is the central hub for all the structured and processed data coming from various sources eg. ERPs, flat files or HDInsights.
  • PowerBI, featuring stunning visualizations and dashboards, offers responsive, interactive reporting for data consumers. PowerBI is also capable of connecting to Event Hub directly for real-time dahboards.
  • A Centralized Data Governance Model (DGM) includes all the essential aspects that must be specified during the project. It is important not just as a one-time DGM setup, but a set of rules and business processes to be maintained after the Data lake implementation.

Flexible, full-stack design

We design and implement data lakes to analyze data in distinct ways, gain insights and create value out of the data your organization generates and imports. Our standard BI Data lake solution implemented on Microsoft Azure platform is based on Lambda Architecture providing flexibility to process either structured data coming from traditional SQL databases or semi/non-structured data ingested from IoT devices, logs, documents.


D Larcthitecture

How We Work

Our implementation methodology involves a mixture of agile and waterfall process management. In the beginning of a Data Lake implementation, we use a waterfall-like method to gather all the required information and set-up the AsIs/ToBe and ToDo lists. With those inputs, Agile scrum project management is used to implement tasks in several sprints. We strongly believe that an efficient Data Lake implementation cannot be successful without involving the client early in the process.

About Starschema

At Starschema we believe that data has the power to change the world and datadriven organizations are leading the way. We help organizations use data to make better business decisions, build smarter products, and deliver more value for their customers, employees and investors. We dig into our customers toughest business problems, design solutions and build the technology needed to compete and profit in a data-driven world.

Demand Forecasting with Latent Matrix Factorization

For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.

Introducing the Stack of the Future for Modern Data Leaders

Fast unobstructed access to data and time to insight matters more now than ever. In these quickly changing times, businesses must innovate and implement a ‘Stack of the Future’ to be able to make accurate, data-driven decisions in minutes, not hours or days. The potential value of data is well known but in the new environment, the ability to easily share and collaborate on data is a competitive differentiator that will be leveraged by forward-thinking companies

COVID–19 Data Set Modeling and Analytics

During times of crisis, companies must look at the available data — both internal and external— and try to understand how that data can be used to determine how the business is currently being impacted, how it is likely to be affected in the future, what are most likely scenarios that will play out, what can be done to counter those scenarios and take advantage of hidden opportunities in this rapidly changing environment. The Starschema COVID-19 dataset ingests reliable data from multiple sources and makes it analytics-ready so it can be easily accessed and used.

A DataOps Journey

Keeping your data platforms running with operational efficiency is both paramount and can be a costly and complicated endeavor. Join us and learn how to apply strategies, techniques, and tools to build a reliable and effective DataOps practice in your organization.


This website uses cookies

To provide you with the best possible experience on our website, we may use cookies, as described here. By clicking accept, closing this banner, or continuing to browse our websites, you consent to the use of such cookies.

I agree