Systems | Development | Analytics | API | Testing

As the world’s number one CRM provider, Salesforce has been transforming customer relationship management for the last two decades across various e-commerce platforms and e-commerce websites. From increased communication to improved customer relationships to better business plans, the Salesforce platform has helped countless organizations move a step above their competitors.

Data warehousing aggregates data from disparate sources so you can run real-time reports for greater business intelligence. However, a data warehouse does more than generate big data analytics. How about using it as a data source rather than just a destination? You can move data from your warehouse to other systems in your networks, such as Salesforce or Zendesk, and improve existing operations.

With more than 150,000 customers and millions of users, Salesforce processes a lot of data — accounts, contacts, activities, leads, opportunities, you name it. And people are adding more data to Salesforce all the time. As organizations realize the benefits of customer relations, Salesforce has become the number one destination for sales, marketing, eCommerce, and field service data. That's even more accounts, contacts, activities, leads, and opportunities.

Python is one of the most in-demand programming languages in the world — and for good reason. Knowing how to code has never been so valuable thanks to the expanding world of tech and focus on data science. From landing high-paying jobs to improving your skillset, learning Python scripting can bring you many opportunities to succeed. However, while these opportunities are robust, many challenges come with learning Python.

Change data capture (CDC) is a crucial, but also tremendously underappreciated, feature that forms the backbone of modern ETL workloads. Without knowing which data has changed since you last accessed it, you’d be forced to extract all the data from a source table or database each time that you perform data integration—which would be a tremendously inefficient process.

APIs are being created faster than ever before with ever-advancing technologies such as node.js and AngularJS. With the flexibility in design and integrations for APIs, there isn't a more exciting time than now to be an API developer. However, with so many new technologies and methods of creating APIs comes the question, "What makes a good API?" While the increase in API creation has many advantages for businesses in multiple areas, there is also more room for low-quality API production.

Integrate.io is a company that provides an ELT (Extract, Load and Transform) data stack. They can do transformations using DBT, which stands for Database Transformation toolkit. Then they use Integrate.io again to push the data into systems like Salesforce. This system will allow you to have better control over your data and provide a cost-effective solution.

ETL (Extract, Transfer and Load) is a well-known data integration process. There is an overwhelming number of tools that you can use (one of which is SSIS) and it can be difficult to choose between them. What exactly is SSIS, and how can it help your company perform ETL better than you ever have before? This article will explain the major features of SSIS, demonstrate the pros and cons of implementing it, and advise as to when you might be better off with a different ETL tool.

Amazon S3 (Simple Storage Service) has been around since 2006. Most use this scalable, cloud-based service for archiving and backing up data. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size. Enterprises value their data as something worth preserving. But much of this data lies inert, in “cold” data lakes, unavailable for analysis. Also called “dark data”, it can hold key insights for enterprises.

Redshift’s JOIN clause is perhaps the second most important clause after SELECT clause, and it is used even more ubiquitously, considering how interconnected a typical application database’s tables are. Due to that connectivity between datasets, data developers require many joins to collect and process all the data points involved in most use cases. Unfortunately, as the number of tables you’re joining in grows, so does the sloth of your query.