Financial inclusion, defined as the availability and accessibility of financial services to underserved communities, is a critical issue facing the banking industry today. According to the World Bank, 1.7 billion adults around the world do not have access to formal financial services, meaning that they cannot open a bank account or access credit, insurance, or other financial products.

Organizations need to manage data across ecosystems, develop data pipelines, APIs, insight into their metadata, and try to make sure that silos and data quality issues are managed effectively. Enter data observability platforms. This blog post looks at what drives many organizations to adopt data observability to ensure the health of your data across systems and providers.

Everything you need to know about the top 15 data integration tools of 2023.

The importance of timely and accurate IT insights is increasing rapidly in the modern era. Organizations often desire a customizable solution to meet the specific needs of their business best and increasingly want to mix data from multiple sources and match data to sales, HR, asset management and other sources in order to better understand root causes and drive better decision-making. To do so, they need a solution that is able to be flexibly tailored for all use cases.

Software testing techniques help us ensure that our software meets all the requirements. The goal of testing techniques is to find out missing requirements, gaps, and errors in comparison to the actual requirements. Finding the right testing technique can be a little challenging. These techniques help us to identify test conditions. Note that there are multiple types of testing. One example is the black box testing technique. In this process, developers test their applications through different inputs.

How to preprocess data using BigQuery ML.

BigQuery BI Engine is a fast, in-memory analysis system for BigQuery currently processing over 2 billion queries per month and growing. BigQuery has its roots in Google's Dremel system and is a data warehouse built with scalability as a goal. On the other hand BI Engine was envisioned with data analysts in mind and focuses on providing value on Gigabyte to sub-Terabyte datasets, with minimal tuning, for real time analytics and BI purposes.