How Doublecloud can help you with streaming analytics?
Doublecloud platform empowers organizations with real-time analytics solutions. It integrates with open-source technologies like Apache Kafka and Clickhouse and captures incoming data streams from various sources using the DoubleCloud Transfer.
‘Transfer’ is an extract and load data service that connects to popular data sources like MySQL, Google BigQuery, Snowflake, and AWS S3. It builds a seamless streaming pipeline between the source and the target, which allows for real-time processing and analytics. Moreover, Datacloud also offers a built-in visualization tool for easy data analysis, and it integrates with various data connectors, including ClickHouse.
Benefits of real-time streaming analytics
Let’s discuss some ways in which streaming analytics benefits organizations.
With self-service analytics, business users utilize the organization’s easy-to-use, existing BI tools to perform relevant analysis. These BI tools have a user-friendly interface allowing users to form complex visualizations easily. Moreover, a real-time data pipeline means users are not dependent on data experts to load and transform data.
Self-service analytics provides business leaders the freedom to build their required reports by removing technical dependencies. This improves business productivity and creates ease of decision-making.
Preset smart rules
Organizations can establish smart rules for incoming data to detect significant business events like anomalies. These rules can trigger corrective measures or other proceeding workflows, such as an ETL process. This brings automation to the data pipeline and improves all existing workflows' accuracy, speed, and efficiency.
Deploy machine learning models
Streaming analytics can benefit machine learning applications in several ways. An efficient machine learning model accepts a continuous data stream and provides continuous outputs. These outputs might be revenue projections or demand forecasts, and a streaming input allows the model to factor in up-to-date information like inflation or consumer price index.
Moreover, machine learning models also need to be constantly monitored for performance. Streaming analytics allows data scientists to analyze the results in real-time and apply corrective measures instantly.
Available at the edge and/or in the cloud
Depending on the business use case and needs, a streaming analytics engine can be deployed on your local systems or in a cloud environment. Both these deployment methods have specific benefits.
A cloud setup enjoys scalability and flexibility to endure high data traffic. Several popular cloud providers like Google and Azure also offer serverless environments with pre-configured analytics to save users from the tedious process of setup and configurations. Finally, cloud services often have industry-standard security protocols, ensuring the data streams are secure from intrusions.
Edge deployment gives users more flexibility for customizing the deployment. Since the entire setup is done from scratch, developers can employ business-specific configurations for improved efficiency. Furthermore, a locally deployed engine reduces network latency issues and allows offline operations.
Coding for advanced use cases
The requirements for data analysis often go beyond the capabilities of pre-defined functions and modules. In this case, custom coding helps cover advanced use cases and achieve specific goals. The custom code performs advanced operations like table joins, data aggregation, and feature engineering. Moreover, custom coding adds flexibility and customization to streaming analytics, providing niche and complex insights.
Limitations of streaming analytics
While streaming analytics enjoy several benefits, it also brings a lot of challenges and limitations.
Many streaming analytics applications are sensitive to the latency of data transmission and analysis. A few seconds' delays can be critical in scenarios like healthcare and industrial work. It can lead to loss of quality, productivity, and health and safety risks.
A streaming data environment needs a reliable network connection and always-up processing hardware. In practical applications, these requirements are challenging to achieve and often limit the system’s usability.
No matter how carefully the pipeline is planned, some form of logical error creeps into the data transformation flow. This is less trouble with traditional analytical methods since the erroneous data sits for some time before it is used in applications. Data engineers can run multiple tests to verify the data quality and integrity.
However, things are different with streaming analytics. If the pipeline contains errors and adequate quality tests are not applied, the corrupt data enters end-user applications. The errors are only detected after they have caused trouble for the customer, causing dissatisfaction.
Irregular data volume
Many streaming analytics applications deal with thousands of events per second. These products gather information from various data sources, such as social media feeds or customer data from e-commerce platforms.
However, the data volume remains inconsistent, and the application can experience sudden spikes. Streaming platforms must be scalable to deal with such surges and ensure unhindered operations.