For public and private entities, data collection is a way of life. That fact has led to the proliferation of common regulations to protect consumers and individuals from unacceptable use or storage of their private data. But it's not just data collection laws companies have to adhere to. There are many US-based and international statutes that put constraints on how they do business. What follows summarizes the most common regulations and how they can affect the work you do, day to day.

The key differences between Talend, MuleSoft, and Xplenty: Enterprise data volumes are increasing by 63 percent per month, according to a recent study. Twenty percent of organizations draw from 1,000 or more data sources. How do these companies extract and move all this data to a centralized destination for business analytics? As we know, Extract, Transform, and Load (ETL) streamlines this entire process. But smaller organizations lack the coding skills required for successful implementation.

A developer's primary job is to work seamlessly, rapidly, and accurately to create software, apps, or websites that match business requirements. Unfortunately, there is a huge margin for error if you have to write lines and lines of complex code. Additionally, many basic tasks in the use of data-related software and other solutions, require extensive coding knowledge that many employees simply don't have. One solution to this is low-code software and development.

Personal Identifiable Information (PII) has become a headache for most digital-first businesses in recent years. Everyone agrees we need rules to keep personal data safe, but there’s no universal PII Data Protection Act we can all follow. Instead, there is a worldwide patchwork of regulations, many of which have global implications. Sweden is one of the pioneers in data security laws.

The key differences between Stitch, Jitterbit, and Xplenty: The average business pulls data from 400 different locations, which makes it tricky to generate valuable data insights. Data-driven organizations use an Extract, Transform, and Load (ETL) platform to pull all this information into a data lake or warehouse for deeper analysis. However, many businesses lack the technical skills (like coding) to facilitate this process. The three tools in this review make ETL workflows easier.

Along with the functionality to make HTTP requests, Xplenty provides various Curl functions and advanced features that can be beneficial in certain use cases. This article covers the Curl functions and features in addition to providing a step-by-step demonstration.

Cybercriminals are currently enjoying a golden age. The sudden shift to remote working, combined with the digitization of everyday life, means endless opportunities to compromise systems and access sensitive data. If you don't want your organization to be their next victim, you must have to know how to prevent data breaches.

During data analysis in SQL, one will often need to look at multiple tables and join them to get the desired results. Joining tables for analyzing data is a required skill for a data scientist or data engineer. Selecting the right join will optimize query performance.

The main differences between Alooma, MuleSoft, and Xplenty: Data-driven organizations pull data from multiple locations such as in-house databases, SaaS, and cloud-based apps, making it difficult to determine accurate business insights. Moving all this information into a single location makes data analytics easier. This is where Extract, Transform, and Load (ETL) comes in.

The key differences between Amazon Kinesis and Kafka are: Introducing data streamers! These services validate and route messages from one application to another, managing workload and message queues effectively. The result? Users process messages through a centralized processor and handle large data streams more efficiently. Amazon Kinesis and Apache Kafka are two data stream services.