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Explore Confluent Self-Guided Tours

Confluent and Carahsoft have partnered to provide a series of self-guided tours for Confluent's enterprise-ready Artificial Intelligence and Cybersecurity solutions. Similar to a live demonstration, these in-depth walkthroughs explore Confluent's wide array of use cases that can help meet you and your organization’s unique IT needs.

 

Learn about Confluent’s Artificial Intelligence and Cybersecurity solutions by starting a self-guided tour below or schedule time with your dedicated Confluent representative for personalized insights.

 

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Confluent AI Self-Guided Tour  

Confluent AI Self-Guided Tour

Confluent’s platform is a full-scale, open source data event streaming platform that empowers agencies to easily access, store and manage data as uninterrupted, real-time streams. Confluent delivers a fully cloud-native experience, upgrading Kafka with enterprise-grade features to boost developer productivity and achieve efficient scalability. Confluent integrates real-time and historical data into a single, centralized source of truth, facilitating the creation of modern event-driven applications, instituting a universal data pipeline that enables robust scalability, performance and accuracy for a wide range of use cases.


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Start a self-guided demo now to learn more about data management, streaming applications and automation.

Confluent’s Benefits Snapshot:

 

  • Real-Time Event Streaming: Seamlessly integrate real-time data with historical data, backed by enterprise scalability, security and performance to make strategic decisions.
  • Universal Data Mobility: Enables agencies to migrate to the cloud at their own pace and maintain persistent data bridge to keep all multicloud, hybrid cloud and on-prem data in sync.
  • Artificial Intelligence: Brings real-time, contextual, highly governed and authoritative data to AI systems and applications.

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Real-Time Data Streaming at Scale

Real-time data (RTD) denotes information processed, consumed, and/or acted upon immediately after generation, representing a newer paradigm in data processing that alters the way agencies operate. Data streaming is the continuous flow of data as it's generated, enabling real-time processing and analysis for immediate insights. Confluent's data streaming platform, allows agencies to take event-driven use cases

Benefits:

  • Get your data to the right place, in the right format, at the right time with greater reusability
  • Combine new data within the context of historical data
  • Empower teams to share and use high-quality data
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Automated, Streaming Data Ingestion Pipelines

Data ingestion involves the extraction, transformation, and loading of data into a target system to enable further insights and analysis. Essentially, data ingestion tools play a vital role in automating and simplifying this process by importing data from diverse sources into systems, databases, or applications. Confluent specializes in automating secure and scalable data ingestion, offering services such as streaming data pipelines, real-time processing, and integration across more than 120 data sources. Users can initiate the streaming of data within minutes, irrespective of the cloud platform they choose.

Benefits:

  • Streamline the import of data from diverse sources into systems, databases, or applications, reducing manual effort and enhancing efficiency
  • Users benefit from Confluent's services by being able to initiate the streaming of data within minutes, regardless of the cloud platform they choose.
  • Improves data quality by ensuring that data is accurate and up-to-date
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Event Driven Architecture

Event-driven architecture (EDA) is a software design pattern designed to facilitate the creation of scalable and loosely connected systems. The flow in this architecture is driven by events, which represent occurrences or changes in the system. These events are generated from various sources, published to an event bus or message broker, and then asynchronously consumed by interested components. Emphasizing flexibility, scalability, and resilience, this approach is effectively implemented by Confluent. Recognized as the optimal solution for event-driven architecture, Confluent provides a comprehensive and scalable platform centered around Apache Kafka. This platform delivers high-performance, fault-tolerant event streaming capabilities and boasts a rich ecosystem of tools, connectors, and management features. As a result, organizations can efficiently build, manage, and scale their event-driven systems with Confluent's empowering solutions.

Benefits:

  • EDA promotes loose coupling between components by decoupling them through the use of events.
  • EDA enables real-time processing and responsiveness by reacting to events as they occur. This ensures that the system can respond quickly to changes, enabling faster decision-making, real-time analytics, and immediate action.
  • Enhances system reliability and fault tolerance by leveraging event-driven communication. Events can be logged and stored in a durable event store, providing an audit trail of past events. This allows for error handling, recovery, and replaying of events, ensuring fault tolerance and system resiliency.
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Real-Time Data Stream Governance

Legacy solutions are often designed with a focus on storage-centric and batch-oriented workloads, making them ill-suited for addressing the requirements of data governance in robust streaming data and event-driven architectures. This limitation prompts the need for a specialized solution. Confluent's Stream Governance empowers agencies to seamlessly amalgamate both current and historical business data, enabling the creation and management of event-driven, real-time solutions. The process of bringing together data from various facets of the organization, maintaining its continuous flow, and unlocking its inherent value demands the right set of tools. These tools cater to the visualization and communication needs of data stewards and governors, facilitating their role in overseeing and managing changes in the data landscape.

Benefits:

  • Enterprise security is inherently multi-faceted and difficult to implement fully. Data governance specifies who manages key business data, who grants access to it, and who makes it available for auditing or another external review.
  • Good governance ensures not only that the organization is doing the right work and doing it well, but that stakeholders and observers hear about it and maintain their goodwill and confidence.
  • Increase collaboration and productivity with self-service data discovery that enables teams to classify, organize, and find the data streams they need
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Real-Time Fraud Detection and Prevention

Confluent empowers agencies to combine and process all of your data at scale for faster and smarter context to detect malicious behavior. Our event-driven architecture delivers a continuous flow of data, chosen by you, and streamed to whichever application or team needs to see it. We provide real-time context to each interaction, each transaction, each anomaly, so your fraud detection systems have the intelligence to get ahead of compromises.

Benefits:

  • Aggregate all your data to build timely context around each of your constituent's profiles
  • Secure and audit data with confidence to avoid steep fines and penalties
  • Provide real-time context to each interaction, each transaction, each anomaly, so your fraud detection systems have the intelligence to get ahead of compromises.
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Data Mesh

Data mesh is a data architecture framework designed to enhance data management and scalability within organizations. It is designed to make data connectivity fast and efficient. Connectivity within the data mesh naturally lends itself to event streaming with Apache Kafka, where high-quality data streams of data products can be consumed in real-time, at scale. As a fully managed, cloud-native Kafka service with the most complete offering that’s available everywhere in the cloud, across clouds, or on-premises, Confluent empowers agencies to build an enterprise data mesh.

Benefits:

  • Increases agility and gives teams autonomy over data
  • Eliminates the bottlenecks with legacy centralized data warehouses
  • Meets modern data requirements
  • Enables the use of real-time data at scale