Data warehouse to generate relevant analytics

A data warehouse is a key aspect of any business. It’s a place where all the data of an enterprise is stored from heterogeneous sources so all the analysis can be made on that data to derive key decisions or form any strategy.

A data warehouse works as a central store where information arrives from the integration of one or more data sources. Data flows into a data warehouse from the transactional system like CCTV camera and other relational databases like MySQL, MongoDB, Neo4j etc.

The data can be classified into :

  • Structured like employee information, inventory management, etc
  • Semi-structured like custom forms etc.
  • Unstructured data like video recording, audio, etc.

The data is extracted, transformed, and loaded so that users can access the processed data in the data warehouse through BI tools, SQL clients, and spreadsheets. A data warehouse merges information coming from different sources into one datastore. By combining all of this information in one place, an organization can examine its customers more holistically. This ensures that it has considered all the information available. Data warehousing helps in making data mining possible.

Three main types of Data Warehouses are:

  1. Enterprise Data Warehouse:
    Enterprise Data Warehouse is a centralized warehouse that provides decision support services across the enterprise. It offers a unified approach for organizing and representing data. It also provide the ability to classify data according to the subject and give access according to those divisions.
  2. Operational Data Store:
    Operational Data Store, which is also called ODS, are nothing but data store required when neither Data warehouse nor OLTP systems support organizations reporting needs. In ODS, Data warehouse is refreshed in real-time. Therefore, it is widely preferred for routine activities like storing records of the employees.
  3. Data Mart:
    A data mart is a subdivision of the data warehouse. It is designed for a particular line of business, such as sales, or finance. In an independent data mart, the collection of data can be done directly from sources.

What we can do?
Sigma Data Systems has a team of expert Data Engineers and data scientists to help you build a robust, secure, and fault-tolerant architecture of your data warehouse. Our data engineers are highly experienced to Construct unique architecture and conceptual data model for a variety of domains and segments.

Tools & Technologies We Use

Here is the list of the tools and technologies we use to provide you the best data warehousing service:

  • Elasticsearch

    It is an open-source, distributed analytics and search engine for all types of data.

  • Oracle

    It is a relational database management system. Oracle is the most flexible and well-designed database for enterprise grid computing.

  • Amazon Redshift:

    It is a fully-managed data warehouse service in the cloud. Amazon Redshift helps companies to store and analyze large amounts of data, up to the petabyte scale.

  • Microsoft Azure:

    It is a cloud computing service created for building, testing, deploying, and managing services & apps through Microsoft-managed data centers.

  • Google BigQuery:

    Google BigQuery is an enterprise data warehouse that helps in storing and querying massive datasets by enabling super-fast SQL queries using the processing power of Google’s infrastructure.

  • Amazon Redshift

    A data mart is a subset of the data warehouse. It specially designed for a particular line of business, such as sales, finance, sales or finance. In an independent data mart, data can collect directly from sources.

  • Snowflake:

    It is a cloud-based data warehousing startup that offers data storage and analytics services.

  • IBM Informix:

    IBM Informix is a scalable database server that manages object-relational, traditional relational, and dimensional databases.

  • Cloudera Altus:

    It is a cloud service platform that allows you to use CDH to analyze and process data at scale within a public cloud infrastructure, including Microsoft Azure and Amazon Web Services (AWS).

  • Talend:

    It is an open-source data integration platform. Talend provides various software and services for Big Data, data integration, data management, data quality, cloud storage, and enterprise application integration.

Benefits Of Data Warehouse

When it comes to the data warehouse, its successful implementation can bring significant benefits to any organization:

  1. Enhanced Data Quality and Consistency:

    Implementation of a data warehouse includes the conversion of data from numerous source systems and data files into a standard format. As each data is centralized from various departments, each department produces results that are in line with other departments. This brings accuracy, quality, and consistency in data.

  2. Enhanced Business Intelligence:

    Merging data from multiple data sources is a common need when conducting business intelligence. For solving this problem, the data warehouse performs the integration of existing data sources and makes them accessible in one place.

  3. Timely Access To Data:

    The data warehouse enables decision-makers and business users to have access to data from many different sources as they need to have access to the data.

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Without big data analytics, companies are blind and deaf.

Geoffrey Moore