Follow Datanami:

Tag: dataops

In Search of the Modern Data Stack

The modern data stack is many things to many people. It’s multi-cloud! It’s the data mesh! It’s BI plus AI! To get a better visibility into just what the modern data stack is, how it’s evolving, and why it all ma Read more…

Why Every Enterprise Needs a Data Fabric & DataOps to Solve Their Data Management Woes

Enterprises today want to have real-time business insights into what’s happening to take actions that ultimately increase operational efficiencies, improve customer engagement, and grow revenue. However, the promise of Read more…

Hands-Off: Manual Data Integration Tasks Plummeting, Gartner Says

While the need to integrate data has never been greater, the addition of machine learning and other forms of automation is driving a large reduction in the amount of manual data management tasks that human workers are re Read more…

Master Data Management: Three Paths to Creating a Successful, Low-Risk Program

The biggest danger to a nascent master data management (MDM) program is starting with the wrong objectives, even though they can often sound quite right. While well-intentioned, unclear business objectives--such as creat Read more…

Bridging the Gaps in Edge Computing

Aside from the lure of the public clouds, which got bigger and stronger in 2020 amid the COVID-19 scourge, the world of data and analytics became a lot more distributed last year. While it seems clear that most AI models Read more…

In Search of Data Observability

The concept of “observability” is well understood as it pertains to DevOps and site reliability engineering (SRE). But what does it mean in the context of data? According to Barr Moses, the CEO of data observability Read more…

A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms

These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or Read more…

Tackling Data Governance in a Multi-Cloud DW World

Microsoft Azure Synapse is the top destination for customers adopting cloud data warehouse over the next 12 to 24 months, followed by Databricks and Amazon Redshift, according to a new data engineering study released tod Read more…

Enabling DataOps for Analytics

Modern enterprises need to quickly deliver the right data to a growing data consumer audience to drive strategic initiatives, often encompassing data science and machine learning, and thereby create competitive advantage Read more…

Running Sideline to Sideline with Big Data

What can you do with big data? A better question might be what can’t you do. From a big data point of view, we are living in extremely resource-rich times, with a huge assortment of tools, framework, and platforms to c Read more…

Zaloni Pivots to DataOps

Zaloni once was focused on helping customers to manage data in Hadoop. But under new CEO Susan Cook, the company has broadened its scope and is now aiming to help customers manage the entire supply chain of data, or what Read more…

Demystifying DataOps: What We Need to Know to Leverage It

The term “DataOps” has picked up momentum and is quickly becoming the new buzz word. But we want it to be more than just a buzz word for your company, after reading this article you will have the knowledge to leverag Read more…

Real-Time Data Streaming, Kafka, and Analytics Part One: Data Streaming 101

The terms “real-time data” and “streaming data” are the latest catch phrases being bandied about by almost every data vendor and company. Everyone wants the world to know that they have access to and are using th Read more…

Hitachi Vantara Buys Cataloger Waterline Data

Hitachi Vantara is acquiring data catalog startup Waterline Data as the U.S. subsidiary of the Japanese industrial giant seeks to meet growing demand for automation frameworks for data lake management via its AI-driven D Read more…

It’s Time for MLOps Standards, Cloudera Says

Just as operational standards have been established for data management via DataOps, the industry needs to create open standards for machine learning operations, or MLOps, according to Cloudera, which today unveiled a ca Read more…

Operationalizing Analytics: Conquering the Last Mile

Per research from McKinsey, only 8% of companies successfully scale analytics. To improve this abysmal rate, organizations must conquer what’s been called the last mile of analytics. For those who can, the payoff is tr Read more…

Paving the Way for DataOps

How is your DataOps going? Have you become a bona fide “insights-driven” business yet? Or are you still struggling to implement DataOps effectively across your organization? If you’re like most, it’s probably the Read more…

StreamSets Eases Spark-ETL Pipeline Development

Apache Spark gives developers a powerful tool for creating data pipelines for ETL workflows, but the framework is complex and can be difficult to troubleshoot. StreamSets is aiming to simplify Spark pipeline development Read more…

Giving DevOps Teeth To Crunch Down on AI Ethics Governance

AI ethics is definitely trending. I’ve seen the phrase in my reading and heard it trip from the tongues of professional acquaintances many times in the past several months. Management fads come and go, and I wonder Read more…

Data Pipeline Automation: The Next Step Forward in DataOps

The industry has largely settled on the notion of a data pipeline as a means of encapsulating the engineering work that goes into collecting, transforming, and preparing data for downstream advanced analytics and machine Read more…

Datanami