Matt Aslett's Analyst Perspectives

Databricks Lakehouse Platform Maximizes Analytical Value

Posted by Matt Aslett on Nov 16, 2022 3:00:00 AM

I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. However, early data lake projects lacked structured data management and processing functionality to support multiple business intelligence efforts as well as data science and even operational applications.

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Topics: Business Intelligence, Data Governance, Data Management, Data, AI & Machine Learning, Streaming Data & Events, analytic data platforms

Cloudera Embraces SaaS with Data Lakehouse Launch

Posted by Matt Aslett on Oct 18, 2022 3:00:00 AM

Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are also shifting from data lakes based on homegrown scripts and code to open standards and open formats, and they are beginning to embrace the structured data-processing functionality that supports data lakehouse capabilities. These trends are driving the evolution of vendor product offerings and strategies, as typified by Cloudera’s recent launch of Cloudera Data Platform (CDP) One, described as a data lakehouse software-as-a-service (SaaS) offering.

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Topics: Business Intelligence, Cloud Computing, Data Governance, Data Management, Data, data operations, AI & Machine Learning, Analytics & Data, analytic data platforms, Operational Data Platforms

Aerospike Has a Data Platform for Real-Time Intelligent Applications

Posted by Matt Aslett on Oct 6, 2022 3:00:00 AM

Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. There are multiple data platform approaches to delivering real-time data processing and analytics, including the use of streaming data and event processing and specialist, real-time analytic data platforms. We also see operational data platform providers, such as Aerospike, adding analytic processing capabilities to support these application requirements via hybrid operational and analytic processing.

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Topics: Business Intelligence, Cloud Computing, Data, AI & Machine Learning, Streaming Data & Events, analytic data platforms, Operational Data Platforms

Neo4j Expands Data Science Focus with New Managed Service

Posted by Matt Aslett on Aug 3, 2022 3:30:00 AM

I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive analytics initiatives are not only developed and trained based on the relationships inherent in operational applications, but also that the resulting intelligence is incorporated into the operational application in real time to support capabilities such as personalization, recommendations and fraud detection. Graph databases already support operational use cases such as social media, fraud detection, customer experience management and recommendation engines. Graph database vendors such as Neo4j are increasingly focused on the role that graph databases can play in supporting data scientists, enabling them to develop, train and run algorithms and machine learning models on graph data in the graph database, rather than extracting it into a separate environment.

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Topics: Business Intelligence, Data, AI and Machine Learning, analytic data platforms, Operational Data Platforms

Data-Driven Agenda for Organizations

Posted by Matt Aslett on Jul 21, 2022 3:00:00 AM

When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them — the likes of Airbnb, DoorDash, ING Bank, Netflix, Spotify, and Uber are often cited as examples — but it is not necessarily clear what separates the data-driven from the rest. Data has been used in decision-making processes for thousands of years, and no business operates without some form of data processing and analytics. As such, although many organizations may aspire to be more data-driven, identifying and defining the steps required to achieve that goal are not necessarily easy. In this Analyst Perspective, I will outline the four key traits that I believe are required for a company to be considered data-driven.

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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, natural language processing, data lakes, AI and Machine Learning, data operations, Streaming Analytics, digital business, data platforms, Analytics & Data, Streaming Data & Events

Dremio Embraces Data Lakehouse with Cloud Launch

Posted by Matt Aslett on Jun 24, 2022 3:00:00 AM

I previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.

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Topics: Analytics, Business Intelligence, Data, data lakes, data platforms

MariaDB Offers One Database for All Workloads

Posted by Matt Aslett on Jun 14, 2022 3:00:00 AM

As I recently described, it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads, albeit with growing demand for hybrid data processing use-cases and functionality. Specialist operational and analytic data platforms have historically been the since preferred option, but there have always been general-purpose databases that could be used for both analytic and operational workloads, with tuning and extensions to meet the specific requirements of each.

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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data platforms, Analytics & Data

Disentangling and Demystifying Data Mesh and Data Fabric

Posted by Matt Aslett on Jun 2, 2022 3:00:00 AM

I recently wrote about the potential benefits of data mesh. As I noted, data mesh is not a product that can be acquired, or even a technical architecture that can be built. It’s an organizational and cultural approach to data ownership, access and governance. While the concept of data mesh is agnostic to the technology used to implement it, technology is clearly an enabler for data mesh. For many organizations, new technological investment and evolution will be required to facilitate adoption of data mesh. Meanwhile, the concept of the data fabric, a technology-driven approach to managing and governing data across distributed environments, is rising in popularity. Although I previously touched on some of the technologies that might be applicable to data mesh, it is worth diving deeper into the data architecture implications of data mesh, and the potential overlap with data fabric.

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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, AI and Machine Learning, data operations, data platforms, Streaming Data & Events

SingleStore Positions Hybrid Data Processing for Data Intensity

Posted by Matt Aslett on May 25, 2022 3:00:00 AM

I recently described the use cases driving interest in hybrid data processing capabilities that enable analysis of data in an operational data platform without impacting operational application performance or requiring data to be extracted to an external analytic data platform. Hybrid data processing functionality is becoming increasingly attractive to aid the development of intelligent applications infused with personalization and artificial intelligence-driven recommendations. These applications can be used to improve customer service; engagement, detect and prevent fraud; and increase operational efficiency. Several database providers now offer hybrid data processing capabilities to support these application requirements. One of the vendors addressing this opportunity is SingleStore.

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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data

Oracle Positions to Address Any and All Data Platform Needs

Posted by Matt Aslett on May 5, 2022 3:00:00 AM

I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.

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Topics: Analytics, Business Intelligence, Data Integration, Data, AI and Machine Learning, data platforms

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