Data in motion - The Central Role of Your Streaming Data Pipeline

In the current landscape of dynamic and diverse data types, the spotlight is increasingly turning towards the critical role played by streaming data pipelines. While the term “large volumes of data” traditionally invoked images of colossal storage solutions, it now extends its reach to the agile and real-time processing of information.

Enterprises universally acknowledge the significance of harnessing insights from streaming data, evident in the widespread deployment of tools across various domains – from Security Information and Event Management (SIEM) systems in Security Operations Centers (SOC) to Application Performance Monitoring (APM) tools in the Application Observability space or various tools in Marketing / Customer Data Analytics. Many have embarked on the journey of establishing a “Data Lake” as a centralized repository, envisioning it as the shared resource for data consumption. Regardless of whether organizations opt for the “analytic tools approach” for specific use cases or delve into the complexities of building an enterprise data lake, a consistent realization prevails – the most formidable challenge lies in efficiently placing data where insights can be systematically extracted. This intricate task falls within the purview of the streaming data pipeline.

Consolidation of the Data Pipelines

Enterprises grapple with deploying multiple data pipelines for distinct analytic tools, leading to complexity and vendor lock-ins. The future solution lies in consolidating these pipelines and channeling data tailored for each use case. These pipelines form core infrastructure components, enabling the extraction of intelligence from real-time data. Key capabilities include:

Integration & Manageability Framework

Data sources utilize diverse methods to share real-time data. A comprehensive framework, accommodating data transmission over networks, storage in files or databases, and publication via APIs, ensures seamless integration with all data sources. A dedicated data pipeline infrastructure offers fine-grained information on data collection statistics, guaranteeing uninterrupted collection.

Data processing on the fly

Centralized data pipelines offer the benefit of collecting and processing data only once. With advancements in real-time processing capabilities, streaming data pipelines facilitate various on-the-fly processing types, simplifying intelligence derivation.

• Data Extraction

• Data Enrichment with static and real time lookups

• Standardization of data structures

• Aggregations

Most standalone analytic tools rely on data at rest querying which slowing them down in the process. Advanced streaming data pipelines perform most of these operation on the fly resulting in improved performance at query time.

Filtering and Routing

Enterprises for various reasons may decide to have data stored on different platforms. Some of these reasons may include,

• Reduce network complexity and data egress costs arising from consolidation.

• Make use best of breed data platforms based on use case demands.

• Adhere to data localization requirements

• Optimize cost of data retention across short term and long term storages.

Data filtering and routing allows enterprises to stream any given slice of their real time data to a downstream data platform. This allows enterprises take complete control of their real time data and make use of the same as per business requirements.

Data Governance

A centralized data infrastructure also provides control over data formats, access and consumption. A data streaming platform allows enterprises to take feeds from sources across different use cases with the ability to maintain controls on what can be accessed by which group of users.

Deploying and maintaining a streaming data pipeline can be tough. Maintaining multiple data pipelines is hence not sustainable. Focusing on the data pipeline can potentially solve multitude of operational challenges while giving flexibility to IT to use the most suited solution for each use case. The governance layer ensures that intelligence from all types of data sources can be leveraged in taking critical business decisions while maintaining strict access controls.