Data Services
DATA ENGINEERING

Put enterprise data to work for the business

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data engineering services
Apexon offers end-to-end Data Life Cycle Management for data consumption patterns

data engineering company
the challenge

Businesses need an efficient and agile way to source and consume data

Enterprises today have access to enormous amounts of data from multi-cloud infrastructures. However, their ability to put that data to work is limited due to increasing complexity, poor data management and ill-equipped infrastructure and tools. Overcoming these obstacles requires several things:

  • Being able to turn a fast-growing pool of enterprise data into actionable intelligence.
  • A trustworthy data foundation and enabling analytics supported by insights from a wide range of data sources.
  • Proper data preparation that allows insights from raw data — for all types of analytics. Insights that are available in context-specific patterns for interactive visualizations, and predictive and prescriptive analytics.

Enterprise Data Management Company
What we do

WE OFFER DATA ENGINEERING SERVICES TO ACCELERATE DIGITAL EVOLUTION

Apexon helps enterprises solve their data challenges, improve end-user satisfaction, and help guide business strategies based on intelligent insights. Our data engineering teams analyze structured, semi-structured, and unstructured data with the right technology, processing tools, and approach. In addition, we provide complete data lifecycle management services, replacing costly and siloed in-house data infrastructure and turning big data pipelines into robust systems prepared for agile business analytics.

  • Our Offerings

    List of our Data Engineering Services

  • Data Collection & Summarization
    Data Collection & Summarization

    Extraction of structured, unstructured data coming from streaming and batch sources and refining/cleansing data to make it available on legacy database or cloud systems, to data scientists and business users for exploration and analysis.

  • Data Storage & ELT / ETL
    Data Storage & ELT / ETL

    Extracting, processing, transforming, and loading data techniques into various relational, non-relational, NoSQL, big data systems and/or cloud storages, depending on data availability, volume, velocity, and type of data.

  • Data Modernization
    Data Modernization & Migration

    An efficient and smart approach for migrating business data to / from on-prem legacy systems into cloud storage infrastructure or new target platforms.

  • Data Pipelines
    Data Pipelines

    Building production-grade repeatable and independent data workflow pipelines to move, transform and store data.

    Using various legacy, Big Data and / or cloud orchestration and data management pipeline tools and techniques like DF, Databricks, Synapse, Informatica, and others, to process data in batch and real time.

  • Continuous Integration & Deployment
    Continuous Integration & Deployment

    Expertise in legacy and cloud-based deployment services for developing efficient production build and release pipelines based on infrastructure-as-code artifacts, reference / application data, database objects (schema definitions, functions, stored procedures, etc.), data pipeline definitions and data validation and transformation logics.

  • Distributed Real- Time Data Processing
    Distributed Real- Time Data Processing

    Expertise in implementing real-time and batch data processing systems across distributed environments based on mobile, web hosting and cloud services.

  • Data Quality
    Data Quality

    Automated data quality solutions for critical tasks, including correction, enrichment, standardization, and de-duplication.

THE OUTCOMES WE DELIVER
Monetize and maximize the value of data

Benefits of our Data Engineering Services

data engineering as a service for faster time-to-value
Faster time-to-value

With accelerators, frameworks, and proven services without compromising quality

Improved Operational Efficiency
Improved Operational Efficiency

Leveraging operational data to improve efficiency; developing ML/AI use cases to improve sales and operations; access distributor data to get better supply chain visibility, identify gaps, and improve replenishment rates

New Customer & Market Insights
New Customer & Market Insights

Access retail data and integrate market research to gain new insights into consumer behavior and inventory levels

data compliance for regulations
User Satisfaction/Empowerment

Self-service analytics model for business users and analysts

Enhanced Compliance
Enhanced Compliance

Integrate regulatory and market data to align sales and distribution

business intelligence engineering to fuel sales initiatives
Increased Sales

Leverage online/e-commerce data to fuel sales initiatives

Our methodology

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how we do it

Our approach

Apexon uses a consultative approach that combines data engineering, cloud, data privacy, and compliance expertise with proprietary frameworks and maturity models to construct a modern data ecosystem. In addition, our flexible resourcing model allows for the rapid scaling of teams through a pod or virtual pod-based approach.

aws

We also leverage pre-engineered accelerators and digital assets across all of our Data Services to accelerate the data transformation journey. These include:

M4 data strategy roadmapM4 Data Strategy Roadmap

A proprietary execution framework for analytics engagements. M4 provides a proven strategy and predictable steps for data modernization. It helps map use cases to modernize architecture and migrate to the cloud while giving all stakeholders a clear view of expectations.

M4 data strategy roadmap

enterprise data management framework and strategy
M1
MAP
  • Map business OKRs and IT goals to build a comprehensive view of strategic intent for the program
  • Map current state challenges with strategic goals to showcase critical milestones (Quarterly view)
  • Map Milestones (Quarterly) with specific data assets, data products and capabilities delivered

M2
MODERNIZE
  • Modernize hosting platform, foundation, data products like DQ engine, catalog, lineage, discovery, virtualization, etc.
  • Modernize data architecture and deliver data models for optimized semantic layer, integrated key management process, data pipeline
  • Modernize information consumption capabilities

M3
MIGRATE
  • Migrate legacy data assets onto target state platform as per reference architecture

M4
MoNETIZE
  • Monetize data assets by empowering business users through governed, self-service capabilities to deliver faster insights

IC4 advanced data analytics platform

A cloud-native, advanced data analytics framework that provides intuitive, flexible self-service access to BI, visualization and analytics insights to help business leaders and analysts make smarter, faster decisions. iC4 provides best-in-class tools for the four foundational principals of information management — Curate, Catalog, Context, and Consume. This stepwise approach and architecture allow organizations to capitalize on the benefits of analytics applications without having to build and maintain a huge data warehouse, visualization capabilities, and reporting platforms.

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data dip framework solves challenges associated with data pipeline validations

Data Dip

This framework solves challenges associated with data pipeline validations. It is distributed in the form of deployable code framework which seamlessly integrates with existing AWS data ecosystems.

Our expertise

DATA ENGINEERING AND ANALYTICS TOOLS

Apexon has experience with the leading cloud and analytics tools, and platforms. We take an agnostic and unbiased approach with the goal of selecting the right tools for the organization and environment. We can help you take full advantage of these tools and platforms to maximize your ROI with them.

google cloud

matlion

python

ibm

data bricks

splunk

kofka

sql server

informatica

Key
Partnerships

aws

We can help you accelerate your time-to-market and increase agility with our comprehensive suite of AWS offerings. Industry-best standards and AWS-guided design patterns drive our AWS cloud solutions. In addition, we adhere to a disciplined continuous review process with experienced and talented AWS-Certified resources.

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azure

Our partnership with Azure helps organizations move to cloud at speed, increasing application availability, technical flexibility, and security improvements.

snowflake

Monetize your data with the help of Apexon’s end-to-end migration and implementation services for Snowflake Data Cloud. Apexon offers end-to-end migration and implementation services for Snowflake Data Cloud, including design, data preparation, re-platforming, and performance optimization.

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Why Apexon

END-TO-END DATA
ENGINEERING SERVICES

From strategy and planning through implementation and managed services for clients from various industries

PROVEN
ACCELERATORS FOR
FASTER TIME-TO-VALUE

Accelerators repository for fast-tracking implementations. In addition, Apexon offers a Data modernization toolkit with best practices for accelerating the data monetization journey

UNMATCHED
EXPERTISE

Highly experienced in democratizing data across heterogeneous systems

FAQ’s – Data Engineering

Data engineering involves the collection, transformation, storage, and retrieval of data in a form that is usable by data analysts, data scientists, and other stakeholders within an organization. It encompasses the processes and techniques used to manage and manipulate large volumes of data efficiently and effectively.

Data engineering has gained popularity due to the exponential growth in the volume and variety of data generated by businesses and individuals. With the increasing reliance on data-driven decision-making, organizations are recognizing the importance of having robust data engineering processes in place to ensure data quality, accessibility, and scalability.

A data engineer is a professional responsible for designing, building, and maintaining the infrastructure and systems required to support the collection, processing, and analysis of data. They possess a combination of technical skills in areas such as database management, data modelling, programming, and distributed computing.

To become a data engineer, one needs proficiency in programming languages like Python, SQL, and Java, along with knowledge of database systems such as SQL, NoSQL, and Hadoop. Additionally, skills in data modelling, ETL (Extract, Transform, Load) processes, cloud computing platforms, and distributed computing frameworks are essential for success in this role.

Examples of data engineering projects include designing and implementing data pipelines for real-time data ingestion, building data warehouses for storage and analysis, developing ETL processes to clean and transform raw data, and optimizing database performance for efficient data retrieval.

Data engineering focuses on the infrastructure and processes necessary to manage and manipulate data effectively, including data storage, processing, and retrieval. On the other hand, data science involves using statistical and analytical techniques to extract insights and knowledge from data, often to inform decision-making or develop predictive models. While data engineers build and maintain the data infrastructure, data scientists utilize this infrastructure to derive insights and solve business problems.

Through our partnership with Apexon, we have been able to achieve many goals. One is to get our platform built with speed by helping our engineering teams and then we have also achieved our infrastructure goals of ISO certifications. Apexon team is helping us deploy the platform even faster from two or three times per week to five or six times a week.
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Mark Fleishman
VP of Infrastructure and Operations, Paige
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Their(Apexon) attention to detail and continued focus on CD Valet has kind of proved that we made the right decision and we have expanded from one team to multiple teams. We are surveying about 31,000 CD rates on a weekly basis and Apexon plays a very important part in that process.
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Yatin Pradhan
VP, Product Management, Seattle Bank
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ib
"Leveraging Apexon’s pre-built data services and metadata models, we delivered a cloud-based insurance analytics platform to our partners in five months. Apexon has the right combination of experience, expertise in industry-standard tools and open source tech stack on the cloud"
Byron Clymer
Chief Information Officer, Lockton Companies
city of dublin
“Vamsi, and his team delivered the first pilot in a matter of two weeks, helping us see the end product before we spent any time and money. We are excited about the future of our partnership.”
Doug McCollough
Chief Information Officer, City of Dublin, OH