StreamSets Data Integration Blog
Where change is welcome.
Data Wrangling for Machine Learning
One can imagine the catastrophe of using inaccurate machine learning models in business – accidents, investment losses, and erroneous analysis. However, because the use cases for machine learning algorithms are numerous and can be positively and negatively impactful, a lot relies on the data quality fed into these models. Before machine learning engineers build machine learning models, the data must…
Documenting the Steps in Your Data Migration Process
Every organization will inevitably migrate data between locations at some point. Data migration refers to the movement of data between storage locations and data platforms. For example, you might need data migration when you introduce new database systems or migrate applications from on-premises to the cloud. Before the evolution of data migration tools, the data migration process was inefficient, lengthy,…
How Operational Data Stores (ODS) and Data Warehouses Work Together
Data lacks value until organizations can gain business intelligence and insights from it. The ability to transform and maximize the value of an organization's data can be challenging for most businesses. Data storage options must hold company data and be available for querying as needed. Why? Statistics indicate that fast and easy data access increases business performance by up to…
The Costs and Disadvantages of Building an ETL From Scratch
ETL and pipelines are at the center of DataOps as they determine a company's success in managing data. One way you can increase your chances of failing at data management is by building an ETL process from scratch without using a platform like StreamSets. In-house ETL may provide specific custom functions, but it is error-prone and requires more time to…
How to Make a Data Pipeline the Easy Way
With their ability to harness and make sense of all types of data from disparate sources, data pipelines are the foundation of modern analytics. A data pipeline refers to a series of steps (typically called jobs) that aggregate data from various sources and formats and validates this data in readiness for analytics and insights. Businesses can choose to build a…
