Big data blog

False Positives & False Negatives: The Critical Importance of Data Quality in Background Checks

A 2024 peer-reviewed study in Criminology found that about 60% of people had at least one false positive in their reports, and nearly 90% had at least one false negative. So they’re systemic issues you have to design around.

Amazon Textract vs Anthropic: PDF to JSON Accuracy, Cost, and Scale

In search of the best tool to extract data from PDF?

We benchmarked Amazon Textract against Anthropic Claude to extract specific data fields from the first two pages of PDF files.

Data Engineering in Background Screening

In 2021, California courts removed date-of-birth information from online search portals to address privacy concerns. For background screening companies, that single change broke long-standing identity matching workflows overnight.

Top Background Screening Trends in 2025: Product Side

The background screening industry is going through another year of change. Global economic landscape, consolidations, new regulations, and fast-moving technology are all shaping how companies compete.

Scala Interview Question List: A Guide for HRs, Tech Leads & Business Stakeholders

Hiring Scala engineers isn’t just about checking off tech skills. You need someone who knows their way around distributed systems and fits your team’s workflow, culture, and project dynamics.

How to Hire Scala Developers for Big Data Projects

Scala has become a go-to language for data engineering. It blends object-oriented flexibility with functional capabilities, making it ideal for scalable, high-performance data pipelines.

Scala Market Overview 2025

Some say Scala’s past its prime. That it lost to Kotlin. That Python dominates the big data domain, and Go took the backend.

And yet—look what stands behind high-performance data platforms, financial systems, and distributed apps. Scala’s still right there.

What is Scala and Why It Matters for Big Data Projects

Back in the early 2000s, Martin Odersky—who had already contributed to Java’s generics—wanted to create something to address the shortcomings of Java. So he built Scala.

How Much Do ETL Systems Cost? Factors & Cost Breakdown

Consider ETL implementation? Then you’re probably thinking about cost—understandably!

In our experience, smaller and mid-sized firms usually budget between $20,000 and $100,000 annually for cloud-based, fully managed ETL solutions.

Common ETL Challenges and How to Overcome Them

You’re deep into an ETL project, and suddenly, data discrepancies start to surface. 

Reports don’t match up, and dashboards look off.

Frustrating, right?

Why Intsurfing is the Best Partner for Your ETL Projects

Intsurfing builds scalable, high-performance ETL solutions to complex data needs. With over 210 projects delivered, our expertise spans data extraction, transformation, loading, and optimization for cloud environments.

Optimizing AWS ETL Pipeline: What You Need to Know

You’ve set up an ETL pipeline on AWS. It works. Data moves. Reports generate.

But over time, latency spikes.

How to Implement an ETL Solution: A Step-by-Step Guide

Data-driven companies don’t rely on a single source of information. They pull insights from competitor data, open datasets, proprietary reports, and industry aggregators. 

Building Scalable Data Pipelines in the Cloud: Best Practices

You’ve built a data pipeline.

Everything’s running just fine—until traffic surges or new data sources appear.

Here, your pipeline starts choking.

PDF to Text Conversion Using PDF2Image and PyTesseract

Turning PDF into text is tricky. 

Sometimes, the text isn’t even text—it’s embedded in images.

Other times, the formatting turns into a mess as you try to process it. 

Best ETL Tools in 2025: Choosing the Right One for Your Business

Businesses generate and collect massive amounts of data. In 2024, this number has reached 149 zettabytes.

But raw data alone doesn’t do any good.

How ETL Data Transformation Helps Businesses Convert Unstructured Data into Strategic Decisions

According to a Deloitte survey, 64% of businesses rely on internal systems and resources for their data. Only 18% explore unstructured data from the web

A Beginner’s Guide to Extracting, Transforming, and Loading Data

Businesses are surrounded by more data than ever before.

But here is the thing.

Some data sits comfortably in internal systems—CRMs, ERPs, and databases.

How to Use Anthropic to Parse Data from PDF

Let’s say you’ve got millions of PDFs and you need to parse unstructured data from them.

Sounds not that hard, right?

Companies That Use Big Data Technologies

Even with financial uncertainties, 56% of data leaders chose to raise their budgets for big data and analytics initiatives.

Our Collaboration Models: Managed Team & Outsourcing

Every business reaches a point where it needs to scale its data infrastructure.

ETL Essentials: A Guide to Efficient Data Management

Just eight years ago, only a third of businesses used data to identify new opportunities and predict trends.

Today, companies analyze between 37% to 40% of their data, and 97.2% invest in big data solutions .

Four Data Validation Techniques to Improve Data Quality

Bad data is a costly problem—organizations lose around $12.9 million each year because of it.

What Is a Data Pipeline? Delving into Data Pipeline Architecture

A data pipeline is a system that moves data from one place to another while transforming it along the way.