Analytics

Building Business Continuity into Hybrid-Cloud

In today’s rapidly evolving digital landscape, businesses face numerous challenges when it comes to data management and protection. Defining effective backup, disaster recovery (DR), and business continuity objectives in particular, stand out as crucial. At Hitachi Vantara, we understand the importance of ensuring data resilience and uninterrupted operations for businesses that have embraced or are considering cloud or hybrid-cloud architectures.

Building a data team in a tight labor market

In this segment, Sumathi Thiyagarajan of the Milwaukee Bucks discusses the challenge of building a diverse data team in a tight labor market, taking a broad approach to hiring from inside and outside the sports industry to get a mix of skills and mindsets. She emphasizes the opportunities for visibility and growth in a small, collaborative organization focused on enhancing the fan experience through data.

Yellowfin Signals Walkthrough

Yellowfin Signals automates data discovery by trawling your business’ data for statistically significant changes and notifying you of the ones that are relevant to your role including trend changes, period comparisons, spikes, dips and more. You’ll be automatically alerted to the most important changes as they happen so you can act immediately. Plus, a signal comes complete with natural language explanation and additional analysis on correlated data changes so you can uncover the root cause fast.

Improving Data Quality: CDC and Hard/Soft Deletes by Integrate.io

When your data systems don’t have access to accurate and real-time data, your organization runs the risk of making bad and costly decisions based on poor-quality business intelligence. In fact, Gartner research director, Mei Yang Selvage, recently said that the failure “to measure the impact results in reactive responses to data quality issues, missed business growth opportunities, increased risks, and lower ROI.”

Kensu + Matillion: A Technical Deep Dive

Kensu is the first solution to bring advanced data observability capabilities to support Matillion, empowering organizations to gain richer insights into their data pipelines and ultimately strengthening trust and data productivity. Matillion ETL is a popular tool for building and orchestrating data integration workflows. It simplifies extracting data from various sources, transforming it according to business requirements, and loading it into a cloud data platform.

Stream Processing Simplified: An Inside Look at Flink for Kafka Users

There was a huge amount of buzz about Apache Flink® at this year’s Kafka Summit London. From an action-packed keynote to standing-room only breakout sessions, it's clear that the Apache Kafka® community is hungry to learn more about Flink and how the stream processing framework fits into the modern data streaming stack.

Model Observability and ML Monitoring: Key Differences and Best Practices

AI has fundamentally changed the way business functions. Adoption of AI has more than doubled in the past five years, with enterprises engaging in increasingly advanced practices to scale and accelerate AI applications to production. As ML models become increasingly complex and integral to critical decision-making processes, ensuring their optimal performance and reliability has become a paramount concern for technology leaders.

The value of data observability to the data analyst

At the beginning of my career as a data analyst, I had to rely on other team members when something went wrong in our data pipeline, often only finding out about it after the event. That experience was one of the driving factors for me to join Kensu. When I spoke with the team for the first time, I had that “lightbulb moment”: data observability is a way of providing help to various data team members, including data analysts, in making their lives more productive and less painful.