Data Warehousing

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Next-Generation Data Architecture

A data warehouse is a central data repository, and it is a relevant component of business intelligence and data analytics. It is an essential source for data specialists to run their analyses.

Information streams from systems, social media, and other different sources into this central source of truth.

Business analysts, data engineers, data scientists, and leaders access this data through business intelligence (BI) tools, SQL commands, and other investigation applications.

Multidimensional Models with meta data advanced storage

OLTP, OLAP, Data Mining and BI

Enterprise data warehouse is mainly used for data mining. This operation involves finding patterns in the data to transform it into valuable information that will help the company improve its business processes.

A well-implemented data warehouse system allows different departments to access each other’s data efficiently and securely with row-level security features. For example, the marketing team can evaluate the sales team’s data to adjust their campaign strategies. It also keeps your transactional systems (OLTP) separated from your analytical systems (OLAP), to ensure very minimal to no performance issues on your daily tasks.

At deltAlyz, we can offer you a solid implementation/modernization of your data warehouse, to maximize your performance and assure the data quality for your business intelligence (BI) and any data science projects you might have.

Simplicity

With a simplified data repository, you will be faster. Consolidate data from multiple sources and file formats into a single trusted database.

Historical Data

A data warehouse is a valuable historical data archiving platform. With it you are able to analyze your company’s past performance with fact-based information to improve your future results with a better decision-making process.

Enhance Query Performance

Working with many different data types from a variety of sources can slow down your queries. A data warehouse can be an excellent solution to enhance your data infrastructure performance and improve analytics.

More Options, Lower Costs

Although an on-premise data warehouse solution can provide good results, you can expect more performance and scalability while reducing your cost using a cloud-based solution.

Companies Can Use Data Warehouses To Query, Analyze, And Report. These Analytics Operations Can Provide A Deeper Understanding Of Production Processes, Operational Inefficiencies, And Customers Needs.

Main Data Types In A Data Warehouse

01.

STRUCTURED DATA

Structured data, such as SQL tables, is quantifiable data that can be neatly organized into rows and columns, for example, sales records and customers contacts.

02.

SEMI-STRUCTURED DATA

This type of data has significantly increased after the expansion of the internet. Most web applications and websites use this type of data in their communications. It can be defined as a type of structured data that does not have a fixed tabular structure. For example, you can think of a table where not all registers (rows) have the same number of columns. XML and JSON formats are examples of semi-structured data.

03.

UNSTRUCTURED DATA

It is data that is not easy to manage and analyze. Think about the written content (such as blog posts or answers in open survey questions), images, videos and audio files, and PDF documents. If you want to store purely unstructured data, you should consider using a data lake instead of a data warehouse.

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