Personally identifiable information (PII) and protected health information (PHI) are two types of sensitive data that fall under one or more data privacy regulations. HIPAA and GDPR are examples of the regulations that govern what organizations can and need to do with PII and PHI. When you work with large data sets, it can be challenging to maintain compliance with these regulations.
With its low-code and no-code features, Xplenty brings the power of ETL and data integration to the masses. But even with Xplenty’s tremendously user-friendly interface, it’s possible that the transformations you design don’t work exactly as you intended—which means you need to debug and resolve the issue fast. Fortunately, there are multiple debugging options in Xplenty for exactly this reason.
The faster you can extract, transform, and load data from MongoDB, the better it is for your business processes and business intelligence systems. The problem is, most ETL solutions struggle to manage MongoDB’s dynamic schemas, NoSQL support, and JSON data types. That’s not the case with Xplenty – which was optimized for easy, no-fuss MongoDB integrations with ease: no custom code, no delays, no confusion.
Heroku is a powerful platform for application development. Users can build and deploy on the cloud, and you can effortlessly scale up once your app takes off. And behind every app, you'll find an equally powerful database: Heroku Postgres. If you're building Heroku apps, you'll find them to be a rich source of operational and customer data. Add in the right Business Intelligence (BI) tools, and you'll be able to derive insights about the inner workings of your organization.
Data integration has been around for decades in some form or fashion, as organizations are always looking for ways to combine their enterprise data and collect it in a centralized location. The most commonly used and dominant type of data integration is ETL (extract, transform, load). ETL first extracts data from one or more source systems, transforms it as necessary, and then loads it into a target warehouse or data lake.
When it comes to data storage, there is almost as much diversity in the types of databases as there is in the data that they contain. Designing and implementing a strong enterprise data strategy means that you need to be aware of the different databases and how you might best apply them within your organization. In IT, the term "flat file" means something very different from the heavy-duty steel construction file cabinets that you might buy from Safco.
In 2011, Pope John Paul II was beatified, Prince William married Kate Middleton, "Game of Thrones" premiered, and Xplenty was born. On a quiet sycamore tree-lined street in Tel Aviv, Israel, breathing distance from Kiryat Sefer Park, the then-startup had just launched a game-changing Extract, Transform, Load (ETL) tool to process, transform, and move data at speed and generate big data analytics at scale. It would become the most advanced data pipeline platform on the planet.
As companies grow and become more data-dependent, data engineers find themselves in huge demand. Employers are snapping up all the best data engineering talent they can find, and some businesses have invested in fast-track professional development paths for DBAs and other more junior data positions. But here’s the thing — data engineers work best when they’re part of a balanced team, just like every other professional. Some organizations overlook this point.
Reports and records. Sales sheets and spreadsheets. Files and financials. Your team has more big data than you can comprehend spread across multiple data sources in more locations than a James Bond movie. Isn't it time you kept this data somewhere safe? Moving data to a data warehouse like Snowflake is like keeping thousands of books in a library or a trove of treasure in an underground vault. Big data, your most prized asset, will be safe and snug.
MongoDB is a popular non-relational (a.k.a NoSQL) database. It is document-oriented and distributed in nature. MongoDB is known to be highly scalable and flexible. In this post, we'll demonstrate how you can utilize MongoDB in your ETL pipelines with Xplenty. To start with, let us briefly discuss why and when you'd want to use MongoDB over other relational databases.
