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Anomaly detection forms an essential component of real-time analytics, which help enterprises gain significant actionable insights across a wide variety of application domains. However, detecting anomalies accurately can be difficult. As systems evolve, behaviors change, and software gets updated, the system needs to be upgraded continuously to detect an anomaly effectively.
This white paper explores the know-hows of big data anomaly detection in real-time.
Highlights:
Spark has eclipsed MapReduce as the preferred processing engine in the enterprise due to its speed, real-time data processing capabilities, and utility in machine learning applications. However, despite this rapid growth in popularity, there is a lag in the availability of Spark technical talent and skills.
In this research paper, Ovum analyst explores how StreamAnalytix Lite bridges this talent gap, making Spark development accessible to developers and other semitechnical personas who do not have pre-existing in-depth Spark knowledge or skills.
StreamAnalytix Lite comes with a highly visual Integrated Development Environment (IDE), which enables users to build, test, and run Spark applications with a code-free drag-and-drop interface.
Read the paper to learn why free and easy to download StreamAnalytix Lite is a natural stepping stone to the fully-featured paid StreamAnalytix platform.
Ensuring that the data is well managed, secure, and accessible are some of the critical requirements for organizations relying on data as a core asset for decision making. Therefore, data governance plays a crucial role in formulating effective data strategies for organizations.
Tracing data to its origin is one of the fundamental requirements in any data governance strategy. However, with complex operations taking place within multiple batches and real-time data flow, understanding data lifecycles and the ability to visualize complete data flows is critical to trace data origins.
This white paper explores the challenges of data governance and how integrating Cloudera Navigator within StreamAnalytix can address those challenges.
Highlights:
As the Internet of Things generates incessant data, organizations need smarter and more efficient ways to manage and process fast-growing data volumes.
With modern IoT operations, organizations can analyze unprecedented amounts of data from devices and sensors in real-time. But how can current approaches to data streaming help businesses manage these continuous information channels? And how well equipped are organizations for data visualization of batch and streaming data?
This whitepaper aims to answer these questions and delve into the importance of streaming analytics for today’s organizations. It will also highlight how:
Massive volumes of customer data are being generated every second. To derive valuable insights from these real-time data streams, businesses must first be able to access a unified, single screen view of the customer journey. Yet, more and more brands are struggling to achieve this unified view.
How can modern enterprises get access to a single-screen view that provides a complete picture of every customer’s past, present, and future? How can they turn transactional customer interactions into personalized conversations in real-time?
This whitepaper explores why real-time customer 360 is imperative for today’s organizations. It also highlights:
Enterprises are increasingly adopting Spark for tasks ranging from ingestion, ETL, and data processing to advanced analytics and machine learning. But despite its growing popularity, Apache Spark is complex and the learning curve is steep.
Data-driven enterprises can now rely on a low-code solution that provides an alternative to time-consuming and tedious manual programming.
Find out how StreamAnalytix provides a practical and viable alternative to the complexities of building enterprise-grade Spark applications. Read this whitepaper to learn:
As businesses continue to grow, existing enterprise data warehouses are faced with the challenge of storing and synthesizing increasing data volumes. Legacy platforms are neither easily scalable nor flexible, and enterprises lack the data integrity needed to make accurate and timely business decisions.
A Single Source of Truth (SSoT) is the answer to these challenges. But how can a centralized data warehouse architecture model help reduce support and maintenance costs, while enabling business users to adapt to the new platform? And how well equipped are enterprises for data visualization of batch and streaming data?
In this whitepaper, we will explore:
Read the white paper to learn more.
Enterprises have been dependent on traditional data warehouses to ingest, model, and store data for ages. In a typical IT environment, conventional data warehouses would extract, transform, and load (ETL) jobs to process data in batches. However, handling millions of queries per month comes at a considerable cost. Blending exabytes of data from various historical and streaming sources such as internal data across spreadsheets, third-party data, and big data stores also makes business analysis difficult and time-consuming.
As businesses explore options to shift from traditional data warehouses to meet their demands and scale business operations, cloud platforms have gained popularity. Whether it is public, private, or hybrid, enterprises are continuing to move their workloads and applications to the cloud infrastructure. Gartner predicts that more than 50 percent of organizations using the cloud today will have all their workloads in the cloud by 2021.
Enterprises will no longer lift-and-shift into the cloud but will instead refactor and rebuild directly in the cloud. While enterprises might retain some mission-critical workloads on-premise, most enterprise data will be in the cloud.
In this white paper, we explore:
Read the white paper to learn more.
