Data engineering is a broad practice and involves many people, processes, and technologies. You may not find all the capabilities you need in one single solution; ProGalor would provide you a data platform, methodologies, technologies and team of experts that can address the majority of your requirements.
Our Approach is to assess and analyse current data and systems around them, before we put model and strategy to transform into integrated platform system. Once data is transformed, data engineers need to make sure it is delivered appropriately to users within and outside of your organisation.
Your data management environment should allow you to seamlessly and securely share data internally among authorised users, departments, and subsidiaries, as well as externally with partners, suppliers, vendors, and even customers.
To do this efficiently, your data management environment should allow you to extend live access to any subset of your data to any number of data consumers, inside and outside of your organisation. You’ll want a data environment in which all database objects are centrally maintained, governed, and secured.
ProGalor offers a platform and team of experts to deliver solutions that engineer the heart of every information system: the Database. We propose goal-driven design process modelling approach that is based on the transformational paradigm according to which each design process transforms a set of products into another efficient, robust and reusable set of products. Our non-deterministic and semi-procedural modelling approaches provide an edge to solve data problems in a reliable way.
ProGalor brings the combination of industry, business and technical experience to satisfy your most demanding database engineering needs.
ProGalor fulfils the need for powerful but flexible process models and for tools that is able to support businesses. That is why we proposed an executable processes model which is both procedural and non-deterministic for supporting database engineering methodology.
Range of industries, and many more, needed data engineering
Travel and Aviation
Business Services
Consumer Products
Financial Services
Energy
Transport & Logistics
We can help you, when:
- Full life cycle development and testing of new and existing Database and Historian Applications required optimising different top layer systems.
- Development of interfaces with external Historian systems.
- Migrating old technology Database systems to latest technology Database systems; that also require development to scripts and tools for data migrations.
- Need assistance with troubleshooting problems related to the operation of mission critical, real-time systems.
- Creating processes for Data Life Cycle management.
- Defining data standards / reference architectures for use by specific project teams in developing technical design.
- Architect database structures for optimal storage and retrieval
- Defining a Meta-Data strategy to describe and administer data, and provide data linkage tracking ability.
- Defining and identifying reference data, and providing strategy and process to harmonise Master Data creation, cleansing, and access across enterprise.
- Developing and maintaining data interoperability standards and interfaces.
- Reverse engineering current systems to analyse set of input and output of systems; and design new criteria and paradigm.
Our Approach to Modern Data Engineering Practices
Take a close look at the current state of data engineering within your organisation. Which part of your current architecture needs to be modified or enhanced to support more robust data pipelines? What analytics initiative should you start with?
Identify a manageable use case — ideally one that will have an immediate impact on your business. Urgent projects often center around operations improvement, such as identifying inaccurate data or fixing an inefficient data pipeline that takes too long to run. Longer-term initiatives focus on business future growth.
In both cases, we ask, how you can extend your current technology assets? What tools have you invested in? Where can you benefit the most by replacing legacy tools with modern technology? Start with one project, and move on to the next. Gradually, think about how you can establish an extensible architecture that leverages the data, tools, and capabilities you have in place while incorporating the modern tools, processes, and procedures.
Data is fundamental to the workings of the enterprise. But if you want to push your data to consumers fast with limited resources, you need to simplify your data architecture.
Depending on your data complexity, access points, colocations, and size; Managed cloud services could be a roadmap, and that will allow businesses large and small to dynamically expand and contract their information systems instantly and near-infinitely, automatically or on the fly.
We consult right technologies that may be hosted by a cloud vendor or in-premises (depending on your data size and investment to acquire best recommended solutions) but that is flexible enough to meet your needs.
We experienced data engineering is a team sport.
How do you enlist the right team members? Follow the data. Appeal to the users, managers, and departments that have the most to gain.
We would build allegiances, engage analysts, and consistent efforts to acquire high-level support from executives and directors.
As the data engineering efforts expand over the period of the project moving from small to big activities and engagements, we have plans about how you can scale the team. Leverage existing data engineering resources as well as skilled IT personnel who may be eager to move into new roles.
Consistently, review the requirements by welcoming approved changes through change management processes, and build the skill sets you need to support data ingestion, preparation, transformation, exploration, and delivery, ideally based on DataOps procedures and continuous integration/continuous delivery (CI/CD) methods.
Once we have alignment with business and IT, would identify product owners to oversee data quality.
We will apply data governance, data security, curation, lineage, and other data management practices.
Answer to questions: Does your organisation already have a DevOps strategy? Find out who spearheads this effort and if they are familiar with the principles of DataOps as well?
We will DataOps practices to set good data governance foundations so you can empower users to self-serve as they prepare, explore, analyse, and model their data, using fresh data in good quality.
At the same time, we approach each data engineering project, with close attention to the following:
»» What industry or data privacy regulations must be observed?
»» Who are the data stewards, and how will they set up processes and workflows to enforce data governance and integrity?
»» Can we enforce role-based access and fine-grained privacy control?
»» Do you have data lineage capabilities for tracing the journey data follows from original source to final destination?
»» Can you organise data so discovery is enabled for your users?
and many more due diligence activities…
Depending on factors such as data size, access points colocations, centralised and technologies in use; it’s often better to process data once it reaches its destination, especially if that destination is a scalable cloud service. Either way, transforming data requires lots of compute resources. Whenever possible, we leverage the modern data processing capabilities of the cloud.
The best cloud data platforms include scalable pipeline services that can ingest streaming and batch data. They enable a wide variety of concurrent workloads, including data warehouses, data lakes, data pipelines, and data exchanges, as well as facilitating business intelligence, data science, and analytics applications.
We recommend to Consolidating data into a single source of truth, whether it be in a single location or across multiple repositories, makes it easier for data consumers. Once data is in one place, it’s easier to access, analyse, and share with other constituents in the cloud ecosystem.
It also allows data team to shift their focus from managing infrastructure to easily managing data as a single source of truth.
As we select data engineering technologies, design data pipelines, and establish new data architectures, plan about how you can serve your organisation’s current needs while positioning it for what lies ahead, including more advanced data science initiatives such as machine learning and deep learning.
In the modern enterprise, data is everywhere, and everybody is a decision maker. How can you extend data-driven decision making capabilities to executives, managers, and individual contributors across your organisation? These knowledge workers shouldn’t have to look for data. It should be infused naturally into the apps they use every day, and presented within the context of their day-to-day activities.
The goal of our data engineering services is to move beyond serving a small group of data scientists and analysts, and to empower the other 90 percent of workers who depend on data to do their jobs. How do you get there? Adopt a product mindset. Strive to impact corporate goals connected with generating revenue, maximising efficiency, and helping your people discover new opportunities for your organisation.
Recommend strategies about how you can share, monetise, and exchange corporate data to create new business value.