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.
How much do you know about Domain-Driven Design (DDD)? It's a design approach to software development where the language and structure of software code match the business domain. The concept comes from a 2003 book by Eric Evans. And it influences software architects, information architects, data engineers, and computer science professionals who organize code and solve some seriously stressful software problems. Domain-Driven Design is a super-successful concept with brilliant business logic benefits.
When COVID hit, multinationals went into a tailspin, scrambling for solutions to pandemic-related problems like suspended flights, social distancing, and stay-at-home orders. How could global brands function when operations are so interconnected? One global sales and marketing brand stayed calm in the crisis, innovating localized strategies that strengthened remote regional teams.
Heroku is a powerful PaaS that helps developers easily and quickly launch and scale modern applications. In addition to its base features, it also offers Heroku Elements Marketplace, which has add-ons for expanding the functionality of the base platform. While Heroku has built-in logging for your applications in this platform, you may find that the default interface leaves something to be desired.
Most enterprises are leveraging vast reserves of data to improve their business insights and decision-making. However, as companies manage larger stores of data and move more and more information from operational databases to data warehouses, it creates an ever-mounting threat of data breaches.
