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.

The Health Insurance Portability and Accountability Act, or HIPAA, is a federal regulation in the United States that protects healthcare data containing personal health information, or PHI. It also covers Electronic PHI, or E-PHI, which are digital records of this information. The ability to effectively using healthcare data is essential for improving patient outcomes, quality of care, resource allocation, revenues, and other operations.

A Big Data stack has several layers that take your data from source to analytics tools. Extract, Transform, Load tools integrate data from sources into a data warehouse or lake. Business intelligence solutions use centralized data for their analytic needs. An ETL tool such as Xplenty offers a user-friendly experience for ingesting data from many sources, transforming it as needed, and sending it to the next layer. Here’s how you can implement this handy tool in your organization.

The major differences between Jitterbit, MuleSoft, and Xplenty: Extract, Transform, and Load (ETL) streamlines data integration by consolidating data from multiple sources, turning it into useful formats, and loading it into a centralized location. The world's most successful organizations use ETL to tame big data, produce visual data flows, and garner business-critical analytics. But with so many ETL tools on the market, which one should you choose?

Today we are delighted to introduce our new Workspaces feature that allows users to organize and group their packages together. It’s always been a bit challenging to organize packages within Xplenty especially if you have hundreds of packages, or if there are many users using the account. Finally, all those issues should be addressed by the new Xplenty Workspace feature.

According to the Ponemon Institutes, "Cost of a Data Breach" report, a data breach's total global cost averaged $3.86 million in 2020. However, a data breach's implications go far beyond financial losses; it can severely hinder an organization's operational capacity and compliance structures.

According to The Economist, “the world’s most valuable resource is no longer oil, but data.” Despite the value of enterprise data, much has been written about the so-called “data science shortage”: the supposed lack of professionals with knowledge of how to use and manipulate big data. A 2018 study by LinkedIn estimated that there were more than 151,000 unfilled jobs in the U.S. requiring data science skills.

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain futured. However, the sudden surge in demand comes with its own set of challenges.

The key differences between Hadoop vs. SQL: Organizations rely on big data to power their business, but many teams struggle with the complexities of data management. Thankfully, Hadoop and SQL handle large data sets more efficiently. These tools manage data in unique ways, which makes it difficult for us to compare them on a like-for-like basis. However, organizations looking to streamline their tech stacks might have reason to choose one over the other. In this article, we compared Hadoop vs.

Large and small businesses face daily reminders that their data’s security is under attack by digital pirates, hackers, and cybercriminals. These threats require an immediate response, whether it's an unexpected hacking threat or a newly discovered database vulnerability. In this guide, we’ll help you understand these threats and how to prevent them in 2021.

We're living in a data-driven age. In every sector, we've seen new companies emerge, executing lightning-fast strategies based on sophisticated analytics. These data mavericks have disrupted and sometimes even devoured their more traditional rivals. To stay afloat, you need a state-of-the-art data infrastructure. That means having the right platforms, the right data pipelines, and the right analytics engines. But when you have all that data, what do you actually do with it?

There are a few standard structured data formats and discussions galore on which of them is more advantageous. Within Xplenty, users are able to process JSON and XML data formats with ease, and this article shares an example showing the functions that facilitate processing XML on Xplenty.

Every business wants to stay agile. They invest in analytics to learn about their customers and their internal state, and they use these insights to make bold and innovative decisions. But then they run into a common problem: how to put those decisions into action. This is where a solutions analyst comes in. These multi-talented creative thinkers will look at the current state of play and identify the smartest path forward.

Your data can quickly get out of control when you’re working with multiple cloud storage services and applications throughout your organization. Complex cloud ecosystems can make it difficult to know what data you have, how it’s being managed, whether it’s safe, and how to use it effectively. Cloud data management platforms can stop this frustrating scenario in its tracks.

The key differences between Fivetran, MuleSoft, and Xplenty: Hiring a data scientist or engineer can cost up to $140,000 per year —something many businesses can't afford. Still, organizations need to pull data from different locations into a data lake or warehouse for business insights. An Extract, Transform, and Load (ETL) platform makes this process easier, but few organizations have the technical or coding know-how to make it happen.

Key differences between Hive and SQL: Big data requires powerful tools. Successful organizations query, manage and analyze thousands of data sets from hundreds of data sources. This is where tools like Hive and SQL come in. Although very different, both query and program big data. But which tool is right for your organization? In this review, we compare Hive vs. SQL on features, prices, support, user scores, and more.

There's an old XKCD cartoon that describes a conversation between a manager and a software developer. This kind of conversation happens all the time. Business leaders know their strategic goals. IT people know what the tech can do. But aligning goals with technology is an ongoing challenge.This is where solution architects come in. They act as a bridge between the business and technical side, and they figure out how to get things done.