DWH AND BIG DATA
Data Warehouse (DWH) is a system that collects, integrates, and stores data from various sources for analytical purposes. Big Data is a term that refers to large and complex datasets that require advanced tools and techniques to process and analyze. Both DWH and Big Data are essential for businesses and organizations that want to gain insights from their data and make informed decisions.
One of the main challenges of DWH and Big Data is to manage the data quality, security, and governance. This requires the involvement of specialists in different areas, such as:
- Data engineers: They design, build, and maintain the data pipelines that extract, transform, and load (ETL) data from various sources into the DWH or Big Data platform. They also ensure the scalability, reliability, and performance of the data infrastructure.
- Data analysts: They query, explore, and visualize the data stored in the DWH or Big Data platform using various tools and languages. They also perform descriptive and diagnostic analytics to answer business questions and generate reports and dashboards.
- Data scientists: They apply advanced statistical and machine learning methods to the data stored in the DWH or Big Data platform to discover patterns, trends, and anomalies. They also perform predictive and prescriptive analytics to provide recommendations and solutions for business problems.
- Data stewards: They define and enforce the data quality, security, and governance policies and standards for the DWH or Big Data platform. They also monitor and audit the data lifecycle and ensure compliance with regulations and best practices.
There are different types of databases that can be used for DWH and Big Data, depending on the nature and volume of the data, such as:
- Relational databases: They store data in tables with predefined schemas and support structured query language (SQL) for data manipulation. They are suitable for DWH that handle structured or semi-structured data with low to medium volume and velocity.
- NoSQL databases: They store data in various formats, such as key-value pairs, documents, graphs, or columns, without fixed schemas. They support non-SQL languages or APIs for data manipulation. They are suitable for Big Data that handle unstructured or semi-structured data with high volume, velocity, or variety.
- NewSQL databases: They combine the features of relational and NoSQL databases, such as SQL support, ACID transactions, scalability, and flexibility. They are suitable for DWH or Big Data that handle structured or semi-structured data with high volume and velocity.
DWH and Big Data are needed by any business or organization that wants to leverage their data assets for competitive advantage and innovation. Some of the benefits of DWH and Big Data are:
- Improved decision making: DWH and Big Data enable businesses and organizations to access and analyze their data from multiple sources and perspectives, which can help them make better decisions based on facts and evidence.
- Enhanced customer experience: DWH and Big Data enable businesses and organizations to understand their customers' needs, preferences, behaviors, and feedbacks, which can help them improve their products, services, marketing strategies, and customer satisfaction.
- Increased operational efficiency: DWH and Big Data enable businesses and organizations to optimize their processes, workflows, resources, costs, and risks, which can help them increase their productivity, quality, performance, and profitability.
Do you want to place an order with our specialists? Contact Us - we will make your dream come true.