Digital transformation is the driving force behind the modern business success. We produce in-depth analysis focusing on data architecture, data monetization, process optimization, and value-added system. We cant fix everything in a day  but we can give you comprehensive insight into areas of your company that would benefit from modernization and refactoring.

Data Replication

Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility, and to improve system resilience and reliability. One common use of data replication is for disaster recovery, to ensure that an accurate backup always exists in case of a catastrophe, hardware failure, or a system breach where data is compromised. Having a replica can also make data access faster, especially in organizations with many locations. Users in Asia or Europe may experience latency when reading data in North American data centers. Putting a replica of the data closer to the user can improve access times and balance the network load. Replicated data can also improve and optimize server performance. When businesses run multiple replicas on multiple servers, users can access data faster. Additionally, by directing all read operations to a replica, administrators can save processing cycles on the primary server for more resource-intensive write operations.


When it comes to data analytics, data replication has yet another meaning. Data-driven organizations replicate data from multiple sources into data warehouses, where they use them to power business intelligence (BI) tools.

    Benefits of data replication

    By making data available on multiple hosts or data centers, data replication facilitates the large-scale sharing of data among systems and distributes the network load among multisite systems. Organizations can expect to see benefits including:


    • Improved reliability and availability: If one system goes down due to faulty hardware, malware attack, or another problem, the data can be accessed from a different site.
    • Improved network performance: Having the same data in multiple locations can lower data access latency, since required data can be retrieved closer to where the transaction is executing.
    • Increased data analytics support: Replicating data to a data warehouse empowers distributed analytics teams to work on common projects for business intelligence.
    • Improved test system performance: Data replication facilitates the distribution and synchronization of data for test systems that demand fast data accessibility.

    Data Pipelines

    What is a data pipeline?


    A data pipeline is a set of tools and activities for moving data from one system with its method of data storage and processing to another system in which it can be stored and managed differently. Moreover, pipelines allow for automatically getting information from many disparate sources, then transforming and consolidating it in one high-performing data storage.

    Data Lake

    Predefined Dashboards & Analysis
    Adhoc Analysis
    Analytical Aggregations

    Business Purpose Oriented Aggregations


    For Partner Access


    For Public Access

    Configurable, Metadata Driven, Flexible Data Lake
    Predefined Gateway Adapters
    Data Sources

    ERP, Airport Operations, Airline Systems, Industry Data, HR Systems, Capital Improvement, GIS, Beacon and Location Based Systems, Legal and safety, Big Data

    Data Warehouse

    Since 1980s, data warehouse concept has born and it was developed to help transition data from merely powering operations to fueling decision support systems that reveal business intelligence. A data warehouse is a large collection of business data used to help an organization make decisions. The large amount of data in data warehouses comes from different places such as internal applications such as marketing, sales, and finance; customer-facing apps; and external partner systems, among others. On a technical level, a data warehouse pulls data periodically (such as daily, weekly, monthly) from those apps and systems; then, the data goes through formatting and import processes to match the data already in the warehouse. The data warehouse stores this processed data so it’s ready for decision makers to access. How frequently data pulls occur, or how data is formatted, etc., will vary depending on the needs of your organization.

    The future of the data warehouse:


    Move to the cloud As businesses make the move to the cloud, so too do their databases and data warehousing tools. Hexalytics cloud solution offers many advantages: flexibility, collaboration, and accessibility from anywhere, anytime. Popular tools like Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, Google BigQuery, and have all offered businesses simple ways to warehouse and analyze your cloud data.


    The cloud model lowers the barriers to entry — especially cost, complexity, and lengthy time-to-value — that have traditionally limited the adoption and successful use of data warehousing technology. It permits an organization to scale up or scale down — to turn on or turn off — data warehouse capacity as needed. Plus, it’s fast and easy to get started with a cloud data warehouse. Doing so requires neither a huge up-front investment nor a time-consuming (and no less costly) deployment process.


    Hexalytics cloud data warehouse architecture largely eliminates the risks endemic to the on-premises data warehouse paradigm. You don’t have to budget for and procure hardware and software. You don’t have to set aside a budget line item for annual maintenance and support. In the cloud, the cost considerations that have traditionally preoccupied data warehouse teams — budgeting for planned and unplanned system upgrades — go away.

    Information Mart

    Information mart usually has the data from the Data Vault and the Business Vault. Data source view needs to be created which is used to access the relational database model in the information mart.


    In most cases, information marts don’t use foreign key references. Therefore, Analysis Services offers the option to create logical relationships from the metadata of the tables found in the information mart. The relationships are found based on the column and table names.


    Hexalytics professionals know how to deal with common issues and how to create common Business Vault entities. We specialized in loading slowly changing dimensions, fact tables and aggregated fact tables, and how to take advantage of point-in-time and bridge tables when providing the information mart. We know a lot of tips and tricks for providing temporal dimensions and performing data cleansing using PIT tables, also how to deal with reference data when loading the dimensional information mart and using hash keys in the dimensional model.

    Data Cloud

    We build Modern analytics architecture with Azure Databricks: Unify data, analytics, and AI workloads. Azure Databricks is the core of this modern data architecture integrating seamlessly with other relevant Azure services.


    Steps with relevant component:

    1. Azure Databricks ingests raw streaming data from Azure Event Hubs.
    2. Azure Data Factory loads raw batch data into Azure Data Lake Storage.
    3. Azure Data Lake Storage/Delta Lake format: 3A. ADLS stores batch and streaming data of structured, unstructured, and semi-structured type 3B. Azure Databricks uses Delta Lake format to keep the raw/refined/aggregated data in Bronze (raw data) stage, Silver stage ( cleaned, filtered data), Gold (aggregated data for business analytics) stages.
    4. Azure Databricks/MLflow: using these, 'Data scientists' performs Data preparation/exploration, Model preparation/training on this data.
    5. Deploying/storing machine learning models:
      1. Azure Databricks stores information about models in the MLflow Model Registry. The registry makes models available through batch, streaming, and REST APIs.
      2. Models can be deployed in Azure Machine Learning web services or Azure Kubernetes Service (AKS).
    1. Azure Databricks SQL Analytics: Run SQL queries on the data lake using query editor. This service:
    2. Power BI generates reports for visualizing the underlying data using built-in Azure Databricks connector.
    3. Azure Synapse SQL pools: Export gold data into SQL pools using Synapse connector.
    4. This analytics architecture uses Azure services for collaboration, performance, reliability, governance, and security:
      1. Azure Purview: For data discovery services, sensitive data classification, and governance insights across the data estate.
      2. Azure DevOps: For CI/CD
      3. Azure Key Vault securely manages secrets, keys, and certificates.
      4. Azure Active Directory (Azure AD) provides user management & single sign-on (SSO) for Azure Databricks users.
      5. Azure Monitor: collects and analyzes Azure resource telemetry.
      6. Azure Cost Management and Billing: For financial governance services for Azure workloads.
    Your data possess tremendous value that needs to be harnessed to understand various aspects of your business. Understanding the impact of your business is crucial. We help you to uncover insights that you only see when you bring all the data together in one place. Modern, modular, scalable data pipelines are crucial part of an enterprise architecture. We help you build efficient data pipelines that will become the lifeline of your enterprise.


    Meet our partners

    Delivering value-added to our clients through trusted, world-renowned partners. Our collaboration and partnership with these companies allow us to provide high-end resources and expertise, fulfilling our mission of innovation through data.


    If you are interested in a career at Hexalytics, you’ve come to the right place. Browse our listings below to see our current opportunities or fill out an employee form to share your skills and interests. We are always on the lookout for new talent.


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    Data Replication

    Data replication is the process of storing the same data in multiple locations to improve data...

    Data Pipelines

    A data pipeline is a set of tools and activities for moving data from one system...

    Data Lake

    With Hexalytics Self-Service tool, the power to solve is a simple drag-and-drop experience...

    Data Warehouse

    Since 1980s, data warehouse concept has born and it was developed to help transition data from...

    Information Mart

    We use modern tools and technologies to solve traditional problems in the decision-making space...

    Data Cloud

    We build Modern analytics architecture with Azure Databricks: Unify data, analytics, and AI workloads...