Real Time data streaming is the ability is the continuous flow of data generated by various sources. By using stream processing technology, data streams can be processed, stored, analysed, and acted on in real-time. This is often not feasible with traditional on-premise IT infrastructure, where organisations are restrained by storage technologies, licensing costs and support. On AWS, these constraints are removed by managed database services that offer enterprise performance at opensource cost. As a result, it is not uncommon for applications to run on top of a polyglot data layer choosing the right technology for each workload.
The use of relational databases to normalise data into well-defined tabular structures. This provides a powerful query language, flexible indexing capabilities, strong integrity controls, and the ability to combine data from multiple tables in a fast and efficient manner. Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud with support for many familiar database engines.
Why Would you need this service?
In a fast-paced world, many organisations cannot wait for data to be processed in batch form. Real time fraud detection and stock market platforms, to ride share apps and e-commerce websites rely on real-time data streams. Paired with streaming data, applications evolve to not only integrate data, but process, filter, analyse, and react to that data in real-time. This is useful for recommendations, or a seamless shopping experience across multiple devices that updates as you shop. Any industry that deals with big data benefit from using continuous, real-time data streaming.
Relational databases enable users to easily categorise and store data that can later be queried and filtered to extract specific information for reports. Relational databases are also easy to extend and are not reliant on physical organisation.
How we deliver this service
Where any type of data to be processed, stored, or analysed, a stream processing system like Apache Kafka can help leverage your data. If you do not have the manpower or expertise to build your own stream processing applications, 1Tech can help.
1Tech has seen a trend where organisations are building a hybrid model that combines a real-time layer and a batch layer. Here data is first processed by a streaming data platform such as Amazon Kinesis to extract real-time insights, and then persisted into a store like S3, where it can be transformed and loaded for a variety of batch processing use cases.
It is important that you choose the right database technology for your needs, 1Tech can help you make decisions about which solutions to use and help you define the functionality you need. We will also work with you to determine whether your developers are familiar with data streaming an relational databases, what the skills gaps exist and how to address these.
1Tech can also help with a view of the associated database technology license costs and whether these costs consider application development investment, storage, and usage costs over time?
- Relational database option we offer include:MySQL, MariaDB Server, PostgreSQL;
- Cost models;
- Architecture design;
- Integration services;
- A view of Customer/user activity;
- Monitoring and reporting on internal IT systems;
- Log Monitoring: Troubleshooting systems, servers, devices;
- SIEM (Security Information and Event Management): analysing logs and real-time event data for monitoring, metrics, and threat detection;
- Machine learning and A.I;
- Predictive analytics;
- Amazon MSK is a fully managed service for building and running applications that use Apache Kafka to process streaming data.
Benefits/ Typical Outcomes
- Beneficial in most scenarios where new, dynamic data is generated on a continual basis;
- Process data streams to produce simple reports, and perform simple actions in response; such as emitting alarms when key measures exceed certain thresholds;
- Data analysis, like applying machine learning algorithms, and extract deeper insights from the data;
- Complex stream and event processing algorithms.
- Accuracy: Data is stored just once, eliminating data duplication;
- Flexibility: Complex queries are easy for users to carry out;
- Collaboration: Multiple users can access the same database;
- Trust: Relational database models are mature and well-understood;
- Ease of use: The revision of any information as tables consisting of rows and columns is much easier to understand;
- Flexibility: Different tables from which information has to be linked and extracted can be easily manipulated;
- Precision: The usage of relational algebra and relational calculus in the manipulation of the relations between the tables ensures that there is no ambiguity;
- Security control and authorisation;
- Data independence is achieved more easily;
- Data Manipulation.