Getting a data analytics role involves the application of tools and techniques, and it is important we understand common tools available and how they can be used in today’s business. We will discuss on some category tools that will be used for data analysis.
OLAP TOOLS
Online Analytical Processing TOOL (OLAP) is a technology that is used to organize large database, optimize query, create reports, and support business intelligence.
They are usually divided into cubes, and each cube are usually organized and designed by a cube administrator which makes it easier to create pivot Chart and pivot Table.
OLAP databases usually contains two types of data and they are: Measures which are numeric data, such as the quantities and average, that can be used to make informed decisions and dimensions are used to organize the measures.
The following describes the major components of a cube
- Cube: A data structure that aggregates measures by the levels and hierarchies. The cubes combines dimensions such as customer, territory with summarized data such as sales, profit, total quantity.
- Measures: They are set of values in a cube that can be aggregated, pre-processed, and analyzed. An examples of such includes: Sales, and total profit amount
- Member: An item could represent unique and non-unique item. 2007 or 2008 represents unique members in a year, and also non-unique numbers such as months for example: January where you can have more than one more occurrence.
- Calculated Member: A member of a dimension, whose value is calculated at run time by using an expressions. An example could be calculated members, profit, and can be determined by subtracting the values of the member, cost, from the values of the member sales.
- Dimension: Dimensions are usually descriptive, and can be sometimes organized in the hierarchies of levels in a cube that are used as base for data analysis. Dimensions includes level for Product Category, Product Sub-Category, and Products.
- Hierarchy: This is a logical structure that organizes the member of a dimension such that each member, has one parent member and zero or more child member.
- Level: Within a hierarchy, data are usually organized within higher or lower of details, such as Year, Quarter, Month, and Day level in time hierarchy.
There are various examples of OLAP tools which includes the following: ORACLE OLAP, Microsoft SQL Server Analysis, IBM Cognos, Integrate.io
DATAWAREHOUSE TOOLS
Data warehouse is a centralized repository of integrated data, from a single or disparate data source. They usually stores currents and historical data and are used for reporting and analysis. The following are the common types of data warehouse available:
- Microsoft Azure: Microsoft Azure is a limitless analytics service, that brings together data integration, enterprise data warehousing and big data analytics. This allows you to query data based on your data terms, using either serverless and dedication options.
- Google Big Query – This is a multi-cloud data warehouse, to power your data-driven innovation, you can query data in real time and get up-date information on all your business process. You can also securely and share insight in your organization. You can build machine learning model, do Multi cloud analysis with Big Query Omni, and Geospatial analysis with Big Query GIS.
- Amazon RedShift- This is the most widely used cloud data warehouse, and uses SQL to analyse structured and semi-structured data across data warehouse, operational databases, and data lakes using AWS-designed hardware, and machine learning to deliver the best price performance at any scale.
- Amazon RedShift- This is the most widely used cloud data warehouse, and uses SQL to analyse structured and semi-structured data across data warehouse, operational databases, and data lakes using AWS-designed hardware, and machine learning to deliver the best price performance at any scale.
ETL TOOLS
- ETL (Extraction, Transformation, and Load) tools are software applications that allows one to perform various operations in datasets and then loads in a data warehouse. They perform Extract, Load, and Transform from various data sources
- SSIS- Microsoft Server Integration Services Package (SSIS) is a product developed by Microsoft. SSIS allows you to create data from a flat file, reformat data, into a fact table. This is a platform for building high-performance integration solutions, which include extraction, loading, and transformation (ETL) packages for data warehousing. It provides graphical tools for building and debugging packages. It provides data sources and destination for extracting, loading, and transformation of data for cleaning, aggregating, merging, and copying data.
- AZURE DATA FACTORY- This is commonly referred to as ADF that allows you to integrate all data sources with built-in connectors at no cost. ADF allows you to easily construct ETL AND ELT processes code-free in an intuitive environment
- AZURE DATA FACTORY- This is commonly referred to as ADF that allows you to integrate all data sources with built-in connectors at no cost. ADF allows you to easily construct ETL AND ELT processes code-free in an intuitive environment
DATA MODELLING TOOLS
Data Modelling can be used in various context, but in the world of data analytics it provides a way of representing your data using visual representation, and analysing the data objects and their relationships to other object.
- Lucid Charts- You can visualize ideas with Lucid charts, this allows you start diagramming, collaborate with team by co-authoring, point out key metrics in existing diagram and integrate with other applications.
- MySQL Workbench – The MySQL workbench is a integrated visual tool, used by database architects, developers, and database designers that provides a data modelling environment, for SQL development and comprehensive administrative tools for server configurations, user administration, and much more.
- ER/Studio – The ER studio allows you to create model from conceptual model or modifying existing assets by making changes to reverse engineered model, user-data friendly. architecture, and design tool make the following data modelling use cases quicker, easier, more accurate and collaborate.
- ER Data Modeller – Graphically design databases by using entity relationship diagrams and automatically generates the most popular SQL databases.
- Microsoft Visio- Visio allows you to create flow charts, which helps bring your ideas to life, it has vast amount of Libraries, shapes, stencils, and templates and turns numbers and information into stories. It also allows integration with other Microsoft office Products.
AUTOMATION TOOLS
Automation tools allows you to automate repetitive task which allows you to visualize a process flow by creating a flowchart.
- Power Automate– This tool allows you build automated process with flows, with drag and drop tools and low codes. It has inbuilt connectors that allows you to automate repetitive and mundane task with ease. It also has inbuilt AI that makes automation faster, which provides document automation, detect images and text, with pre-built models.
- UI Path -This tool provides automation capabilities that allows you build and manage high ROI pipeline in one place. It provides a low-code build environment from simple to advanced automation with API integrations and API rich tools. It creates an environment for automations, and capabilities to maintain, govern, and manage securities. It has inbuilt robot that aligns with operational task, and build apps that makes automation easy. It has a cloud based platform that is easily designed with fast, quick, and simple deployment in mind
- Smartsheet Tools – This tool allows you to manage projects, and automate business Process. It also provides a rich set of view from workflows, reports, and dashboards to capture and track plans, resources, and schedules. Some of the key features includes smart collaboration with team members, workflow automation, content management, secured request management, and streamlined business apps.
PROGRAMMING TOOLS
They are tools that allows you to perform various data analysis task that can either be simple or complex.
- Python & R – They are open-source language, and they are free to access, and can run on all platforms (IOS, windows, and Linux) and can handle various data analysis task.
- Python- This is an high level general purpose programming language, and written in simple natural language. And there are wide variety in its application. Some of the major applications include:
- Data Science and Data Analysis
- Web Application Development
- Automation/Scripting
- R – R provides a software environment, and statistical programming language, built for statistical computing and data visualization. It has numerous capabilities, and can be applied in:
- Manipulating Data
- Statistical Analysis
- Visualizing Data
- SQL – Structured Query Language, allows one to communicate with the database. SQL allows you to store, manage, and manipulate data in the database.
DATABASE MANAGEMENT TOOLS
Database management tools, allows IT professionals to organize and store information in tables and stores within an operating system.
- MySQL – This is an open-source database, that is usually cost effective and delivers high performance, and scalable database applications. MySQL provides various edition, and they include the following:
- MYSQL standard Editions
- MYSQL Enterprise Editions
- MYSQL cluster grade Editions
- SQL Server Management Studio: This provides an integrated environment for managing SQL Infrastructure, from SQL Server to Azure Database. It also helps to administer and monitor instance of SQL instance and database. SSMS is used to deploy, monitor, and upgrade the data-tier components, used by applications, and build queries and scripts
- Oracle RDMS- This is a database commonly used for online transaction processing (OLTP), data warehousing (DW), and mixed (OLTP & DW) database workloads. Database is available, on-premises, on-cloud, or as an hybrid cloud installation.
There are other common DBMS System – Such as Microsoft Access, Postgre SQL, Amazon RDS, Informix.
