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StreamSets

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  • Making Apache Kafka Dead Easy With StreamSets
    Making Apache Kafka Dead Easy With StreamSets
    Clarke Patterson - Head of Product Marketing Recorded: Apr 4 2019 58 mins
    Apache Kafka has become a popular choice for stream processing due to its throughput, availability guarantees and open source distribution. However, it also comes with complexity, and many enterprises struggle to get productive quickly as they attempt to connect Kafka to a diversity of data sources and destination platforms.

    In this webinar, expert Clarke Patterson will discuss and demonstrate best practices that will help make your Kafka deployment successful, including:

    -How to design any-to-any batch and streaming pipelines into and out of Kafka.
    -How to monitor end-to-end dataflows through Kafka.
    -How to operate continuous data movement with agility as data sources and architecture evolve.
  • DataOps for Digital Transformation
    DataOps for Digital Transformation
    Sheryl Kingstone - VP of Customer Experience & Commerce - 451 Research | Sean Anderson - Dir. of Product Marketing - StreamSe Recorded: Apr 2 2019 57 mins
    Every organization wants to know more about its customers. More data can lead to a more comprehensive Customer 360 view—but only if all that data can be captured, managed, and kept safe from unauthorized use.

    Companies today are forced to piece together partial views of customer behavior, managing to the limitations of their systems and not to their analytic goals. When data lives in silos it can create gaps in analytic outputs and poor visibility for business intelligence. StreamSets built a DataOps platform to help manage complex data flows into analytics environments with pre-built connections for popular customer data systems. Join 451’s research director Sheryl Kingstone to discuss how DataOps is fueling digital transformation.

    In this webinar you will learn...

    -How to build Customer 360 in a scalable way to capture customer data—across all an enterprise’s systems, securely and in real-time.
    -How to deliver a unified, complete picture of the organization’s interactions with its customers.
    -Challenges that make it difficult to adequately protect customer data, including personally identifiable information (PII).
  • Avoid Data Drift in Your Cloud Data Warehouse
    Avoid Data Drift in Your Cloud Data Warehouse
    Sean Anderson - Director of Product Marketing - StreamSets | Dash Desai - Technology Evangelist - StreamSets Recorded: Mar 28 2019 51 mins
    Cloud Data Warehouses are on the rise. As companies aim to make analytics pervasive across their organization, cloud data warehouses and data marts became a logical solution for delivering a familiar analytics experience but without the constraints of managing large DW infrastructure. Cloud Data Warehouses help reduce management and infrastructure costs, offload maintenance and uptime responsibilities, and allow users to simply load data and run queries instead of managing databases.

    However, migrating and streaming data to these new cloud managed services remains a freshman effort with many tools offering only simple ingestion functionality and limited data destinations. StreamSets has built an advanced integration with popular cloud Sw solutions like Snowflake, one of the world’s most popular cloud data warehouses. This level of integration provides fast synchronous and asynchronous ingest, multi-table uploads, and data drift compensation. StreamSets Control Hub helps users then manage a variety of pipelines, on-premise; across public clouds and cloud services.

    In this webinar you will...

    -Take a look at common usage patterns for Cloud Data Warehousing.
    -Understand the core functionality of the Snowflake connector.
    -Get information on the installation and operation of the new tool.
  • Hadoop Creator Doug Cutting: How to Operationalize Machine Learning and Advanced
    Hadoop Creator Doug Cutting: How to Operationalize Machine Learning and Advanced
    Doug Cutting - Hadoop Creator & Chief Architect - Cloudera | Arvind Prabhakar - Co-Founder & CTO - StreamSets Recorded: Mar 26 2019 56 mins
    When streaming data meets machine learning and advanced analytics, the innovation possibilities can be endless. Operationalization of data movement in a hybrid cloud architecture is key to making your technology investments deliver on their promises. Without it comes frustrated developers, failed projects and technology disillusionment.

    Join Doug Cutting, Apache Hadoop creator and Chief Architect at Cloudera, and Arvind Prabhakar, co-founder and CTO at StreamSets as they discuss how to use DataOps to avoid common pitfalls associated with adopting modern analytics platforms.
  • If Cloud is the Problem DataOps is the Solution
    If Cloud is the Problem DataOps is the Solution
    Kirit Basu - VP of Product - StreamSets Recorded: Mar 19 2019 44 mins
    According to research firm Gartner, at least 75% of large and global organizations will implement a multicloud-capable hybrid integration platform by 2021. Join StreamSets Head of Product Management, Kirit Basu, and Head of Product Marketing, Clarke Patterson as they discuss how StreamSets customers are taking a DataOps approach to hybrid-cloud integration.

    We explore how Fortune 500 customers are using StreamSets to streamline Apache Kafka and Data Lake projects using principles adopted from DevOps.

    During this webinar, we will discuss:

    -Pitfalls to avoid for any hybrid-cloud project
    -Key requirements to ensure continuous movement of data across any cloud
    -How StreamSets customers are using the platform to drive real value from DataOps
  • Operationalize Data Movement to Supercharge Your Architecture
    Operationalize Data Movement to Supercharge Your Architecture
    Julian Ereth - Research Analyst - Eckerson Group | Arvind Prabhakar - Co-Founder & CTO - StreamSets Recorded: Mar 14 2019 59 mins
    DataOps borrows concepts from agile development to streamline the process of building, deploying and operating dataflow pipelines at scale. Putting DataOps into action requires not only the right technology, but more broadly a thoughtful approach to align the people and the process behind such an initiative.

    Eckerson Group research analyst Julian Ereth join StreamSets' Co-Founder and CTO, Arvind Prabhakar to explore the emerging trend of DataOps.

    During this webinar, we will discuss:

    -Principles and benefits of DataOps
    -Common DataOps use cases
    -Practical guidelines for putting DataOps into action
    -How StreamSets can help on a DataOps journey
  • Modernize Cybersecurity Threat Detection
    Modernize Cybersecurity Threat Detection
    Nathan Necaise - VP of Data Sciences Emerging Services - Optiv Recorded: Mar 12 2019 55 mins
    The convergence of streaming data platforms with cyber security solutions presents real opportunity for combating and predicting future threats. Join StreamSets and Optiv as we discuss common use cases and architectural patterns used by leading Fortune 500 organizations to modernize their cyber architecture.

    During this webinar, we will discuss:

    -Common challenges facing today’s SIEM’s and how to effectively augment them with streaming data platforms
    -Show customer examples and demonstrate how they are leading to transformative effects
    -How to optimize security architectures that use technologies like Splunk using StreamSets
  • Modern Streaming Data Stack with Kinetica & StreamSets
    Modern Streaming Data Stack with Kinetica & StreamSets
    Matt Hawkins - Principal Solutions Architect - Kinetica | Mark Brooks - Solutions Architect - StreamSets Recorded: Mar 7 2019 60 mins
    Enterprises are now faced with wrangling massive volumes of complex, streaming data from a variety of different sources, a new paradigm known as extreme data. However, the traditional data integration model that’s based on structured batch data and stable data movement patterns makes it difficult to analyze extreme data in real-time.

    Join Matt Hawkins, Principal Solutions Architect at Kinetica and Mark Brooks, Solution Engineer at StreamSets as they share how innovative organizations are modernizing their data stacks with StreamSets and Kinetica to enable faster data movement and analysis.

    During this webinar, we will discuss:

    -The modern data architecture required for dealing with extreme data
    -How StreamSets enables continuous data movement and transformation across the enterprise
    -How Kinetica harnesses the power of GPUs to accelerate analytics on streaming data
    -A live demo of StreamSets and Kinetica connector to enable high speed data ingestion, queries and data visualization
  • Ultralight Data Movement for IoT with SDC Edge
    Ultralight Data Movement for IoT with SDC Edge
    Guglielmo Iozzia - Big Data Lead - Optum | Pat Patterson - Technical Director - StreamSets Recorded: Mar 5 2019 34 mins
    Edge computing and the Internet of Things bring great promise, but often just getting data from the edge requires moving mountains.

    During this webinar, we will discuss:

    -How to make edge data ingestion and analytics easier using StreamSets Data Collector edge, an ultralight, platform independent and small-footprint Open Source solution for streaming data from resource-constrained sensors and personal devices (like medical equipment or smartphones) to Apache Kafka, Amazon Kinesis and many others.

    -We'll provide an overview of the SDC Edge main features, supported protocols and available processors for data transformation, insights on how it solves some challenges of traditional approaches to data ingestion, pipeline design basics, a walk-through some practical applications (Android devices and Raspberry Pi) and its integration with other technologies such as StreamSets Data Collector, Apache Kafka, and more.
  • Recipes for Success How to Build Continuous Ingest Pipelines
    Recipes for Success How to Build Continuous Ingest Pipelines
    Arvind Prabhakar - Co-Founder and CTO - StreamSets Recorded: Feb 28 2019 62 mins
    Modern data infrastructures are fed by vast volumes of data, streamed from an ever-changing variety of sources. Standard practice has been to store the data as ingested and force data cleaning onto each consuming application. This approach saddles data scientists and analysts with substantial work, creates delays getting to insights and makes real-time or near-time analysis practically impossible.

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