Analytics

No-Code Data Pipelines: Streamline Data Integration

Historically, connecting multiple data sources to a single destination required extensive experience as a computer programmer or data scientist. Today’s no-code data pipelines have changed that perspective. Now, practically anyone – even those without any coding experience – can use no-code pipelines to streamline data processing without damaging data quality. You will, however, need the right ETL and ELT tools to manage real-time data flows.

Understanding Needs From All Perspectives Before Applying Tech Solutions

When it comes to investing, it’s been said that the biggest risk of all is not taking one. But for many of us, the prospect of figuring out how and where to smartly invest our money is overwhelming if not flat out confusing. TIFIN is a FinTech company focused on democratizing this process by matching investors to potential investments with AI. In this episode Raja Musunuru, the Chief Product Officer of financial tech leader TIFIN, covers a wide range of topics, from the importance of understanding customer needs, to promoting data fluency across an organization.

Why Cloud Operations Would Benefit From a "On-Premise" Approach to Cost Management

Moving operations to the cloud has obvious benefits, but without discipline costs can quickly spiral out of control. In this clip, Raja Musunuru of TIFIN reiterates the importance of understanding usage and the ability to scale. Get even more insights from data and analytics leaders like Raja on The Data Chief.

Without data quality, your data initiatives will fail.

Chad Sanderson is passionate about data quality, and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp. Without data quality, your data initiatives will fail. Despite that, data teams still struggle to gain buy-in on quality initiatives from executive teams. Here's why: 1.

Data Lake Architecture & The Future of Log Analytics

Organizations are leveraging log analytics in the cloud for a variety of use cases, including application performance monitoring, troubleshooting cloud services, user behavior analysis, security operations and threat hunting, forensic network investigation, and supporting regulatory compliance initiatives. But with enterprise data growing at astronomical rates, organizations are finding it increasingly costly, complex, and time-consuming to capture, securely store, and efficiently analyze their log data.