Real-time streaming data is all around us—we just don’t realize it. For most businesses it's NOT just enough to have data; they need to have the right data and leverage it at the right time. This is the time value of data, a critical metric business executives need to think about when working with data and determining how it fuels their applications. For instance, the value of data for applications like Google Maps that provide users with live traffic maps and road alerts degrades over time.
Capturing and using streaming data in real time is the key to situational and contextual awareness in a fast-paced business environment. Knowing when, how, and why events occur in your organization is the key to better decision making, maintaining a competitive advantage, and reducing end-to-end latency from data capture to insight generation for continuous intelligence.
Data streaming also makes AI (artificial intelligence) and ML (machine learning) algorithms responsive by reducing the time to insight – from the moment data is born to when it is used for insights and made actionable. For example, online travel booking agency Booking.com found that a latency increase of 30% costs their business about 0.5% in conversion rates.
Data streaming addresses the velocity dimension of big data for mission critical tasks. Streaming data is unbounded, unordered, and immutable coming from multiple business units, across geographical locations, through different channels, from heterogeneous applications, customers, suppliers, and partners. Streaming is applied across three different areas in enterprises—data integration, data processing, and continuous analytics and ML model serving.
By leveraging streaming as part of AI/ML driven operations, enterprises can make faster decisions and adapt to changing market conditions and business needs while providing better, faster customer experiences and significant ROI (return on investment) for data streaming projects.
According to Confluent’s 2023 Data Streaming Report, 76% of organizations reap 2x-5x in returns with streaming initiatives! Ali Ghodsi, CEO of Databricks, in an episode of A16Z podcast opines that streaming data also solves many data overload problems—as having streaming systems under the hood takes care of a lot of the burden of manual DataOps.
Today most enterprises struggle with the dual challenge of having more data to consume and less time to act, often leaving data unprocessed and losing valuable insights. Data teams need to react faster, provide responsive digital experiences, and incorporate continuous intelligence using streaming data to improve decisioning and maintain a competitive advantage. Today, the traditional batch-based approach of storing data before it’s processed can’t keep up with the demands that modern, digital businesses face.
According to Jay Kreps, CEO and co-founder of Confluent, widespread adoption of streaming technologies is driven by a perfect storm of business trends and pressures. McKinsey also states that moving from batch processing to streaming data processing is a key step in building game-changing data architecture.
For enterprises, building data lakes and data lake houses and doing AI and ML are not enough. Enterprises should leverage the right data, with the right insight, at the right time using streaming platforms to continuously capture, process, and analyze data. This fast-changing data can then be used to incrementally re-train models and remain competitive.
Stream data processing enables organizations to make decisions based on what’s happening now so they can steer their business through an ever-changing digital world. Businesses that combine agile practices with insights from real-time streaming data are positioning themselves to be industry leaders, able to rapidly understand and respond to market threats and opportunities as they happen.
Real-time data has infiltrated our daily lives too. For example, high traffic retail businesses don’t use old-school ERP (Enterprise Resource Planning) systems to replenish their inventories. They are tracked in real time and reordered through an automated system leveraging sales from cash registers and online sources. Financial institutions minimize risks of cyber fraud with innovative detection techniques that rely on real-time streaming data to detect patterns and anomalies that signal the need for immediate action. For example, NORD/LB, one of Germany’s largest banks, fuels their new core banking platform with streaming data and has improved their ability to detect fraud in real time.
Streaming ecosystems, tools, and frameworks have become a nervous system for modern enterprises—collecting, assimilating, and analyzing heterogenous data from a wide variety of sources and channels like emails, social media, clickstreams, point-of-sale transactions, geo-location systems, and more. This data is then used to power high-impact business use cases like fraud detection, product recommendations, personalization, and 360° views of customers, employees, and patients. Data streaming also enhances supply chain visibility by bringing data in real time from customers, suppliers, carriers, warehouses, weather, geo-location, etc.
These use cases process data with a streaming-enabled data stack that ingests, prepares, and analyzes data to deliver insights with high concurrency, performance, and reliability.
If real-time streaming is so essential for business – the question is “Why aren’t more enterprises adopting streaming data architecture?”
The biggest challenge for organizations looking to adopt data streaming has been ensuring a smooth developer experience. Streaming is a paradigm shift where data is first processed and then stored — the opposite of batch processing. Additionally, streaming requires in-depth understanding of critical concepts and patterns that data engineers and architects must consider when building streaming applications to ensure reliability, accuracy, availability, and minimal data loss. Expertise with streaming data can be hard to find, and developers and consultants who are adept at working with Apache Kafka® (the de facto technology for data streaming adopted by over 80% of Fortune 100 companies) are often highly paid experts.
Data management architectures repeatedly reinvent themselves and break new ground to meet the demands and scaling requirements of large enterprises. To do this, underlying infrastructure, operating models, and operationalization must manage and coordinate hidden complexities and dependencies flawlessly 24x7x365.
This has led many organizations to seek out managed services that provide enterprise-grade, fully managed platforms for data streaming that reduce the learning curve for building and managing streaming applications and allow teams to operationalize streaming solutions faster.
Across technology infrastructure, the use of managed services is dramatically rising. At least 60% of organizations will depend on managed services by 2025
The value of managed services lies in their ability to provision distributed infrastructure resources that are constrained, costly, and prone to failures and provide a robust, reliable, highly available, fault-tolerant system that organizations can elastically consume. Managed services for data streaming are not just tools for managing Kafka. They are a full ecosystem of components designed to automatically integrate and ingest data from other systems and build secure, governed, real-time applications that span across data center and cloud boundaries for an end-to-end solution. Operating at scale with demanding workloads comes with risks, and organizations need to trust that their technology stack will be always-on and scale to meet peak demands and run services that are reliable, durable, scalable, and secure.
Streaming solutions often need multi-zone availability for data replication across multiple data centers and automated self-healing to detect and mitigate cloud outages. They often need to auto-rebalance data workloads to maintain SLAs. Managed services implement robust, battle-tested operational processes that include unit and integration tests, load tests, performance tests, and failure tests to simulate faults, ensure reliability, minimize downtime and data loss, and improve resiliency of deployments.
Data streaming solutions also demand durability to ensure data integrity, data quality, and adherence to data contracts—guidelines around adherence of the incoming data to rules, schema, SLAs, accuracy, and completeness. Critical customer data requires auditing, monitoring for data loss, and preventive strategies to proactively repair data integrity issues. Reliability of cloud services is enabled through proactive monitoring and gathering logs, traces for early detection, mitigation, and incident response.
Most managed services for real-time streaming implement security controls like access management and vulnerability scanning to protect and secure customer data. They are also compliant with major regulatory benchmarks like ISO27001, HIPAA, HITRUST, etc. Managed services also include out-of-the-box compliance and regulatory practices and industry specific mandates.
By leveraging managed services for data streaming, customers can rest assured that their data is safe and secure, services are available according to SLAs, and they can scale up and down on demand while lowering TCO (total cost of ownership). Managed data streaming systems provide a complete set of tools to build and launch applications faster, reducing the time to market for streaming applications while maintaining all of these critical requirements.
Confluent provides a managed service for data streaming that abstracts and eliminates the complexity of building streaming solutions with the goal of making streaming adoption, development, and deployments simple and seamless. Confluent Cloud is a cloud-native, fully managed data streaming platform that goes above and beyond open source Kafka to enable streaming ingestion, integration, infinite storage, and processing of continuous data with enterprise-class features, removing the burden of Kafka message broker management and operationalization.
With Confluent, data teams have access to an enterprise-ready, easy-to-use development environment with efficient client libraries and 120+ pre-built connectors to sources and sinks for streaming integration with high resiliency and global availability for mission-critical workloads. Confluent's Kora Engine powers their fully managed data streaming platform to optimize for performance, capacity, failover, and infrastructure management. Users don’t need to capacity plan since Confluent, by default, only provisions the compute and storage required at any given time.
Confluent equips teams with a complete set of enterprise-grade tools and features for upholding strict security and compliance requirements, ensuring trusted data can be shared with the right people, in the right format, with data quality rules and stream-based sharing. The platform includes fine-grained permission management for data access, data observability, and monitoring—which provides a view into how streaming is used across the business and where resources are consumed, allowing for the identification of hotspots in data pipelines.
Confluent’s Kora Engine also powers Confluent Cloud to achieve a 99.99% uptime SLA. And with the company’s recent acquisition of Immerok, organizations can now leverage the power of Apache Flink® for stream processing within the Confluent ecosystem.
Confluent is widely used across industries like financial services, omni-channel retail, manufacturing, and transportation. 10x Banking, a cloud-native core banking platform, has showcased massive success using Confluent Cloud to supercharge their customer experiences with real-time streaming. And manufacturing titan Michelin has reaped 35% savings from reduced Apache Kafka operations costs as a result of adopting Confluent Cloud to replace their on-premises operations.
In today’s fast-evolving data landscape, the ability to harness the power of real-time streaming data is becoming the competitive differentiator for successful organizations across nearly every industry and vertical. The use of managed services and platforms to ease data streaming adoption is the recommended approach for organizations looking to drive value and thrive in their digital transformations. Data streaming is now the new normal. Enterprises that don’t leverage streaming technology are in danger of being left behind. Check out Confluent’s data streaming resources hub for the latest explainer videos, case studies, and industry reports on data streaming.
Check out the recorded sessions from Current 2023 | The Next Generation of Kafka Summit, and hear from subject matter experts across a variety of industries.