Harnessing robust cloud-based applications can help companies increase revenues by more than 30% yearly. To reach this pot of gold, 40% of businesses plan to pick up the pace of their cloud implementations and follow in the footsteps of popular apps like Uber, Netflix, and Lyft.
The only problem is that there are many hurdles and challenges to overcome before enjoying the benefits of a flexible and scalable cloud infrastructure. The first step in your cloud migration journey is to stream huge volumes of data from existing sources to the cloud. Without the right tools and technologies, data streaming can be time-consuming and costly for your engineers.
To make migration happen successfully, your data streaming architecture needs to work hard to provide cloud transitions as fast as possible and continually manage a high volume of data.
What is real-time data streaming?
Real-time data streaming is the constant flow of data produced by multiple sources. It enables you to collect, analyze, and deliver data streams as they generate in real-time. Examples of streaming data include log files produced by users of mobile applications, e-commerce transactions, and telemetry from cloud-based devices.
There are two ways to stream data: batch and real-time. Real-time streaming data is continuously generated, enabling you to use the information for concurrent analysis exactly when you ingest it. Batch processing differs because it receives the data in batches and stores the source until enough data has been collected according to specific parameters. It comes in the form of unending streams of occurrences. This data comes in all sizes, formats, and locations, including on-premise, in the cloud, and a hybrid cloud environment.
What is data streaming architecture?
Data streaming architecture is a framework of software components that consume and process significant amounts of streaming data from many sources. A streaming data architecture ingests data instantly when you create it, continues it to storage, and could include tools for real-time processing, data manipulation, and predictive analysis.
Data streams create vast amounts of data, which is primarily semi-structured and needs a lot of pre-processing to be effective and useful. A data streaming architecture contains several components:
Source: There could be tens of thousands of machines or software programs, otherwise called sources, that rapidly and continuously produce large amounts of data.
Ingestion: Ingestion enables you to capture continuously produced data from thousands of devices reliably and safely.
Storage: Depending on your scale, latency, and processing demands, you can choose a service that will satisfy your storage needs.
Processing: Some processing services require only a few clicks to modify and transport data, allowing you to integrate ML into sophisticated, unique real-time applications.
Analysis: Transmit streaming data to various completely integrated data storage, data warehouses, and analytics services for additional analysis or long-term storage.
What are the use cases of data streaming?
Today’s businesses can’t always rely on batch data processing because it doesn’t allow the visibility they need to monitor data in motion. Data streaming architecture has use cases in almost every sector, from analytics to data science and application integration. This technology is advantageous to every sector that uses big data and can profit from continuous, real-time insights. Business use cases include:
- Business analytics and performance monitoring
- Real-time sales campaign analytics
- Fraud detection
- Customer behavioral analytics
- Supply chain and shipping
What are the benefits of real-time data streaming?
As long as you can scale with the amount of raw data generated, you can acquire valuable insights on data in transit and use historical data or batch data that has been stored. Here are three main use cases of data streaming:
1. Movement of Real-Time Data
As well as examining data as it is ingested, you can store it for further evaluations by data streams from tens of thousands of endpoints and execute ETL operations on massive quantities of continuous, high-speed data in real-time.
2. Processing of Event Streams
The most popular use cases involve change data capture (CDC) and communication between a large number of independent microservices for real-time recording, threat monitoring, and event response.
3. Data Evaluation
Evaluate data as soon as it is generated and allow real-time decisions to improve customer experiences, avoid networking problems, or update your organization in real-time on important business KPIs.
The 6 Essentials for Real-Time Data Streaming Architecture
A flexible streaming architecture simplifies the complexity of conventional data processing architecture into a single self-service product that can convert event streams into data warehouses that are available for analytics. Furthermore, it makes it simpler to keep up with innovation and outperform the competition. Here are the essentials that the best data streaming architecture contains.
Thanks to the rise of cloud-based technologies, data streaming architecture is thrust into the spotlight. It needs to be scalable to keep up with increased data volumes, compliance standards, and shifting company needs as businesses adopt cloud tech.
Scalability is especially important when a system malfunctions. The pace of the log data from each source may go from a few KB to MB, maybe even GB. The quantity of raw data proliferates as additional capacity, resources, and servers are added while programs scale. Hence the need for a scalable data streaming architecture.
2. Fault Tolerance
Fault tolerance is the ability to carry on as normal after a malfunction and enable swift recovery. Your architecture needs advanced systems that transparently recover if a failure occurs. The system’s state must be preserved to ensure no data is lost.
There are checklists you can follow to improve the fault tolerance of your data streaming architecture, such as preventing a single failure point by using data from various sources and in different forms. You can also maintain high availability and endurance while storing streams of data.
3. Real-Time ETL Tools
Process streaming data is a crucial part of big data architecture in companies with large data volumes. Real-time analytics is made possible by a variety of managed service frameworks that build an end-to-end streaming data pipeline in the cloud. In-memory stream processing has significantly advanced streaming ETL. When you have large datasets that need preprocessing before ingestion into your real-time analytics database, it’s the best option.
For example, Equalum enables real-time, in-memory streaming ETL for replication scenarios, analytics, and BI tools for real-time decision-making.
4. Storage Options
Real-time data streaming solutions are built to facilitate distributed processing and reduce consumer and producer dependency. Deployment too tightly coupled to one central cluster can choke the autonomy of projects and domains. As a result, the adoption of streaming services and data usage will be limited. Containerization promotes more flexibility and domain independence in a distributed cloud deployment architecture.
5. Analytics Capabilities
A streaming data analytics database is made explicitly for analytics, which requires it to quickly prepare enormous data streams for queries after ingestion. Even complex query results should return rapidly. Additionally, the number of simultaneous requests must be scalable without causing conflict that hinders your ingest.
For enhanced efficiency, your database should isolate the query processing from the ingest and employ SQL. Even better is a real-time analytics database that can execute rollups, searches, aggregations, joins, and other SQL actions as the data is consumed.
6. Change Data Capture (CDC) Tools
You can continually capture changes made in your operational database (like MongoDB). The problem is that data warehouses are immutable, making it difficult to modify the data and maintain real-time synchronization between the operational database and the data warehouse. This even happens with some of the most well-known cloud data warehouses. To solve this, you can use Equalum. Our solution enables you to continuously access real-time data, track changes, and apply transformations before ETL using built-in CDC capabilities.
High-Speed Data Delivery Becomes a Reality With Equalum
The world revolves around real-time data streaming, which is why reviewing your architecture is more important than ever. Choosing the right components will set your business up for future success by ensuring you can scale up and be flexible as needed. Whether you are planning to migrate to the cloud, harness real-time insights for business KPIs or another use case, data streaming can help you achieve your goals.
Equalum steps in to support businesses on their cloud migration or adoption journey by enabling continuous access to real-time data using built-in CDC capabilities and streaming ETL. With Equalum’s help, better visibility and fast data delivery can be a reality. Want to know how it works? Book a demo today