Providing rapid success on the projects with world-class data engineering, data science and project management knowledge and experience.
Our Databricks Professional Services can help you at any given moment in your data & AI journey
What WE Do?
We offer Databricks consulting and services to organizations that wish to maximize the value of their data. We are providing data science resources and best practices to strengthen your existing teams along with the data engineering skills and technology that enable data science for actionable insights.
If you’re sure how to migrate and implement databricks. With our consulting services we will handhold you through the whole procedure with defined architecture and streamlined processing.
We will work with your business and data teams to help identify several use cases that can be quickly established and productionised on the Databricks platform. Throughout this process, your technical teams will learn the ropes from our consultants by working side-by-side to execute your use cases, guiding them from start to finish.
One of the most frequent barriers to successful data science projects is getting the right data to the data science team. PlatingNum permeates this obstacle with a wealth of data engineering experience and skills. We guarantee that data is integrated, cleansed, secured and quickly accessible to data science teams so that they don’t have to focus on data collection and preparation.
We help to periodically monitor databricks and other data associated with pipelines and make relevant updates to keep your performance level higher.
Azure Databricks: 1-Week Proof of Concept
Platingnum uses Azure Databricks to assist enterprises to expedite their journey from Data Engineering to Data Science.
We assist enterprises in deploying Azure Databricks in the following use cases:
Creating a new data ecosystem: For people who do not have a large data infrastructure and rely on simple reporting and spreadsheets for insight.
Migrating from Spark to Databricks: For people who are currently using Apache Spark on Hadoop or in the cloud and want to boost Spark performance by utilising Azure Databricks.
Using Spark to modernise the data ecosystem: For those who have a legacy and sophisticated big data infrastructure that impedes data engineering and science.
Our Approach – Deliverables
- Kick-off discussion with your team to identify critical use cases for PoC, with an emphasis on improving business choices anticipated to produce value.
- Configure Azure Databricks and load relevant data for use cases. Other Azure capabilities, such as Azure Data Lake Storage for storing source data or Azure Data Factory for pipeline automation, can be included as needed.
- We’ll teach you how to leverage a combination of Spark SQL, R, and Python in Databricks to prepare data for analysis in collaboration with your team.
- Discuss relevant modelling methodologies for the given use cases, taking into account how the results might be utilised to drive better decisions.
- Present choices for delivering the data and models in a repeatable manner, such as through Power BI dashboards that end users may engage with to investigate the model’s most recent findings.