Data Engineering
Data Engineering Consulting Services
We specialize in the below areas and provide top-notch Data Solutions leveraging best tools, practices and paradigms.
Data & AI Strategy
Leverage the power of Data & AI by building robust strategy for generating business value and monetisation of data.
Data Modernization
Leverage cloud hyperscalers and modern data platforms and adopt new paradigms and technologies to increases business functioning.
AI Solutions
Generate higher business value by assessing the readiness of data, understanding the complex AI use cases and seamless ML model deployments.
Data Engineering
Data Pipelines
Robust pipelines reliably collect, process and analyze large data volumes from diverse sources. Leading tools like ETL, Spark, Kafka enable building customized pipelines to fit needs. Optimized pipelines efficiently handle huge data, focus on timely insights, improve analytics and enable faster data-driven decisions.
CD4ML
ML Engineering (MLOps)
We build seamless solutions for building models continuously from experimentation to production while keeping the track of model metrics, model versions and data version.
Intelligent products
Becoming increasingly common in many industries, as businesses seek to provide more value to customers and improve efficiency through the use of AI and ML technologies.
DataOps
We provide DataOps services to experience Scalability, Resiliency, CI-CD pipelines, Automated QA, and Observability of Enterprise Data Operations. We provide effective enterprise data management and governance to reduce downtime and mitigate data risks.
Data platforms
Leverage technology solutions that enable organizations to store, manage, process, and analyze large volumes of data. These platforms typically provide a range of tools and services to support data processing, storage, and analysis, as well as security, governance, and compliance. Data platforms can help organizations to gain valuable insights from their data, make data-driven decisions, and optimize business processes. However, building and maintaining a data platform can be a complex and resource-intensive task, requiring specialized skills and expertise. As such, many organizations are turning to cloud-based data platforms to reduce the cost and complexity of building and maintaining their own data infrastructure.
Data Lifecycle
Refers to the stages that data goes through, from its initial creation to its eventual disposal or archiving which typically consists of the following stages:
Collection > Storage > Processing > Analysis> Distribution > Archiving > Disposal
Optimize your data management processes, improve data quality, and ensure compliance with data privacy regulations. By following best practices for each stage of the data lifecycle, organizations can ensure that their data is properly managed, secure, and used effectively to support business goals.
Data Architectures
Data Mesh
Shift the focus of data management from centralized, monolithic data platforms to a more decentralized approach that emphasizes data autonomy, domain-driven design, and distributed ownership of data. Domain Ownership, Data as product, Self Service data platform, Federated computational governance A newer but promising approach to managing large and complex data ecosystems, particularly in organizations where data is a critical part of the business.
Data Lake
A central data repository that allows organizations to store vast amounts of structured, semi-structured, and unstructured data at scale. Unlike traditional data storage systems, which are typically organized by application or database, a data lake is designed to store data in its raw form, without any predefined structure or organization. Data lakes have become increasingly popular in recent years, as organizations seek to manage and analyzes growing volumes of data. Effective data governance and management practices are critical to ensure that data in the data lake is accurate, reliable, and secure.
Data Lakehouse
A new approach to data management that combines the best features of a data lake with the benefits of a traditional data warehouse. The idea behind a data lakehouse is to create a unified platform that can handle a wide variety of workloads, including batch processing, streaming, and interactive querying. A newer but promising approach to data management, particularly for organizations that need to manage and analyze large volumes of data from a wide variety of sources.
Fast Data (Real time data streams)
An approach to data management and processing that emphasizes speed, low-latency, and real-time responsiveness. The goal of fast data architecture is to enable organizations to process and analyze large volumes of data in real-time, in order to gain insights and make decisions faster. The key features of fast data architecture include: Real-time Processing, Low-latency, Scalability, Fault-Tolerance, Modular Design Fast data architecture is particularly well-suited for applications in areas such as financial services, e-commerce, and online advertising, where real-time data processing and analysis is critical for success.
Data Modeling
Leverage the process of creating a conceptual representation of data and its relationships to other data within an organization's information system. The goal of data modeling is to create a blueprint for designing databases, data warehouses, and other data systems that support the organization's business objectives. Data modeling uses specialized tools and techniques to define data entities, attributes, relationships and constraints. Multiple types of data models, including conceptual, logical, and physical models are created based on the system requirement. Conceptual models provide a high-level view of the data and its relationships, while logical models provide a more detailed view of the data structures and relationships. Physical models provide a detailed view of how the data is stored in a specific database management system. Data modeling is an important step in designing effective data systems, and it helps ensure that the organization's data is accurate, consistent, and can be easily accessed and analyzed.
Data Management
Leverage the processes and technologies to organize and manage data throughout its lifecycle. Ensuring that data is accurate, consistent, secure and accessible to those who need it is the goal of data management.
Data management includes a wide range of activities such as data governance, data quality, data integration, data security, data storage and data analysis. Data management professionals perform these activities to ensure that the data is properly stored, secured and maintained.
It is important for organizations of all sizes and industries to have effective data management as it helps ensure that data is accurate, reliable and can be easily accessed and analyzed.
The key activities involved in data management include:
Data Governance
Involves the development of policies and procedures for managing data, including data ownership, data security, and data quality.
Data Quality
Ensure that data is accurate, complete, and consistent. This can involve activities such as data profiling, data cleansing, and data standardization.
Data Integration
Combine data from different sources into a single view is called data integration. Activities involved are data mapping, data transformation and data aggregation.
Data Security
Protect your application and data from unwanted access, use, and consumption. Data backup and recovery, access control, and data encryption are some of the activities that can be involved.
Data Storage
Store data in a secure and scalable manner. This can involve activities such as database design, data partitioning, and data archiving
Data Analysis
Use data to make informed decisions. Data visualization is one of the activities that is involved along with data mining, and predictive analytics.
