- February 25, 2021
- Posted by: Abhay Das
- Category: Big Data
Improving customer delight while keeping costs low and maintaining a competitive edge has become possible by leveraging the latest Industry 4.0 technologies, especially cloud, data analytics, IoT and the like.
There is an increasing move towards storing data in a hybrid or multi-cloud environment to keep infrastructure costs low while enjoying the benefits cloud offers of flexibility and scalability.
While this has its benefits, such a hybrid environment also brings with it certain limitations. Data is stored in multiple locations and multiple formats and to leverage the data and draw insights for informed decision making, businesses need a unified view.
Data integration is the process by which data from different locations is unified and made usable. With the number of data sources increasing, the need for effective data integration tools is also gaining importance.
With data integration businesses gain:
- Access to a single and reliable version of truth, synchronized and accessible from anywhere
- Access to accurate data that enables effective analysis, forecasting, and decision making
5 Applications of Striim-based Data Integration
A platform such as Striim enables data integration of on-premise and cloud data from Databases, Messaging Systems, Files, Data Lakes, and IoT in real-time and without disrupting operations.
It provides users access to the latest and reliable data from varied sources such as log files, databases, sensors, and messaging systems. Pre-built integration and wizards-based development enables an accelerated building of streaming data pipelines and provides timely insights for improved and data-backed decision making.
The various scenarios where Striim-based data integration can be applied include:
1. Integration Between On-premise and Cloud Data
Businesses migrating data from legacy systems to the cloud can benefit from Striim’s Change Data Capture (CDC). CDC reduces downtime, prevents the locking of the legacy database, and enables real-time data integration (DI) to track and capture modifications to the legacy system, applying the changes to the cloud after the migration is complete.
It also facilitates the continuous synchronization of the two databases. It also allows for data to be moved bi-directionally, with some stored in the cloud and some in the legacy database. For mission-critical systems, the migration can be staggered to minimize risks and business interruptions.
2. Real-time Integration in the Cloud
Businesses opting for cloud data warehouses require real-time integration platforms for real-time data analysis. The data is sourced from both on-prem and cloud-based sources such as logs, transactional databases, and IoT sensors and moved to cloud warehouses. CDC enables ingesting data from these different sources without disrupting data production systems, delivers it to the cloud warehouses with sub-second latency and in a usable form.
Techniques such as denormalization, enrichment, filtering, and masking are used for in-flight processing, which imparts benefits including minimized ETL workload, reduced architecture complexity, and improved regulatory compliance. As synchronizing cloud data warehouses with on-premises relational databases is possible, data is moved to the cloud in a phased migration to reduce disruption to the legacy environment.
3. Cloud Integration for Multi-cloud Environments
Real-time data integration in multiple cloud environments connecting data, infrastructure, and applications improves agility and flexibility to move your data to different data warehouses on different clouds.
4. Enabling Real-time Applications and Operations
With data integration, businesses can run real-time applications (RTA) using on-premise or cloud databases. The functioning of RTAs can seem immediate and current to users because of real-time integration solutions moving data with sub-second latency.
Further, data integration also transforms data, cleans it, and runs analytics, helping RTA further. It can be of use for several applications such as videoconferencing, VoIP, instant messaging, online games, and e-commerce.
5. Anomaly Detection and Forecasting
With real-time data integration, companies can manipulate the IoT data generated by different types of sensor sources, clean it and unify it for further analysis. Among the various types of analytics one can run on a real-time data pipeline, anomaly detection and prediction are important as they enable timely decisions.
These can be of use in many scenarios: for checking the health of machinery and robots in the factories; health of planes, cars, and trucks; cybersecurity to detect and prevent fraudulent transactions, among others.
The use cases are not restricted to the above five. Data integration can support machine learning solutions by reducing the time for cleaning, enriching, and labeling data and ensuring the availability of real and current data. It can help synchronize records across departments, functions and systems and provide access to the latest information.
It can improve an understanding of customers as well as decide the course of marketing strategies. It can also help with faster scaling up and can be a game-changer.
Indium is a Striim implementation partner with more than 20 years of experience in consulting and implementation in leading-edge technologies.
Our team of data scientists and engineers have vast experience in data technologies, integration, and Striim and work with domain experts to create bespoke solutions catering to the specific needs of the customers across industries.
If you have a multi-cloud or hybrid environment and would like to leverage your data stored in different locations more effectively with data integration, contact us now: