Every day new types of data, weblogs, structured and unstructured data is being created. Volumes of data keep growing forcing companies to upgrade their data warehouses and the application database. Innovative businesses use big data to improve business operations and develop their products and services.
Big data involves large sets of structured and unstructured data. It enables the organization to use the emerging technologies and data management strategies to come up with innovative products and services.
Informatica PowerCenter helps to reduce big data management costs as well as handle the growing volumes of data and data complexity. Informatica tools help businesses to achieve a faster, flexible and quality data integration.
Installation of Informatica version 10 for your business will provide with a platform for data integration using ETL tool, data virtualization, big data management, accessing data quality as well as testing the stored data.
Advantages of ETL Informatica tool.
- Provides extra features for data analysis and metadata management for business users.
- Provides an improved user experience.
- Leads to increased performance and reduce big data management cost.
- You can integrate codes with the external software configuration tool
- Different design use cases for code development.
Features ETL-Informatica in big data management
- Emphasize improvements on Eclipse-based developer tool rather than on PowerCenter tool.
- A user-friendly and lightweight developer client is designed which can easily integrate with other technologies.
- Focuses more on big data management platform, developer client, admin console and in analyst tool among others.
- Introduction of Business analyst tool and Administrator/Operator tool to monitor the performance of databases.
- Introduction of Email service allows users to configure the email client for specific needs.
- An independent schedule service feature is used to schedule events within the organization. The schedule services is not a replacement for Control-IM or Autosys.
Characteristics of Big data
- Volume: Large volumes of data stored and generated
- Variety: Different types and nature of the data
- Velocity: The speed at which data is generated and processed to meet the demand of users.
- Veracity: The quality of data captured varies and this can affect the accuracy of data analysis.
Big data architecture
Data repository exist in many forms with each organization capturing data that meets its needs. As big data continuously keep evolving Teradata relational database are being employed. Data is shared through a distributed framework across multiple servers.
The big data architecture is used by businesses as the foundation for data analysis tasks.
Big data architecture is applicable when;
- Extracting data from the extensive network or a weblog
- You want to process data of above 100GB in size
- You want to carry out a big data project which involves third-party products and optimize your environment.
- You want to store large volumes of unstructured data which will later be transformed into structured data for further analysis.
- You have both structured and unstructured data from multiple sources that need to be analyzed.
- You want to analyze data for business needs and decision making.
Planning of the big data architecture
This involves coming up with techniques for data ingestion, protection, processing and transformation of data in a file system. Data analysis tools and queries are designed to mine data from different data sources and the results are output to different data files.
Layers involved in big data architecture
- Big data source layer: Data for big data architecture can come from a variety of sources. Data is collected in real-time or as a batch from the company’s server, sensors or third-party data providers. Other sources for data includes the data warehouse, databases, social media platforms, email subscription, using ERP application or the CRM system.
- Data massaging and storage layer: Data from multiple sources is received in this layer. The unstructured data is converted to a format suitable for use by the analytic tool and later stored in this format. Structured data is stored in a relational database management system (RDBMS) whereas unstructured data is stored in Hadoop distributed file system (HDFS) or in NoSQL database.
- Analysis layer: This layer is used for data analysis. It interacts with the stored data in the database or file system and extracts business intelligence. Several analytic tools are used to analyze data in the big data environment.
- Consumption layer: This layer displays the analyzed data to an appropriate output layer. The information viewed by users of the system or used in various applications and business processes.
Processes across big data architecture
- Connection to data sources: Big data architecture requires adapters and connectors which are used to connect the storage system to various data sources like sensors, social media, databases or third-party networks.
- Governance: the architecture provides data governance by ensuring privacy and security of stored data. Companies are required to comply with the specific data governance mechanisms through signing service level agreements or invest in specialized compliance software.
- System management: big data architecture is built on large volumes of distributed clusters of data. The system administrator needs to continuously monitor system performance and address any system issues via a central management console.
If the organizations store its data in the cloud, the admin spends a lot of time and effort coming up strategies for monitoring and maintaining a health system.
- Quality of services offered: the organization should come up with a platform to define the quality of services (QoS) to offer, compliance policies and any other mechanism for data protection. When dealing with the cloud-based system, the cloud providers should offer QoS on data storage in a distributed data environment.
The big data management system enables users to integrate and secure big data sets in a distributed environment. The big data architecture helps you to extract vital business information from volumes of data at lower costs and risks. It enables organizations to develop, deploy, operate, and manage big data infrastructure.