AWS Big Data Blog

This is a guest blog post co-written by Amit Nayak at Microstrategy. In their own words, “MicroStrategy is the largest independent publicly traded business intelligence (BI) company, with the leading enterprise analytics platform. Our vision is to enable Intelligence Everywhere. MicroStrategy provides modern analytics on an open, comprehensive enterprise platform used by many of the […]

Amazon QuickSight recently added native support for comparative (e.g., year-over-year) and cumulative (e.g., year-to-date) period functions which allow you to easily introduce these calculations in business reporting, trend analysis and time series analysis. This allows authors in QuickSight to implement advanced calculations without having to use complicated date offsets in calculations to achieve such datetime-aware […]

Today’s businesses face an unprecedented growth in the volume of data. A growing portion of the data is generated in real time by IoT devices, websites, business applications, and various other sources. Businesses need to process and analyze this data as soon as it arrives to make business decisions in real time. Amazon Managed Streaming […]

Data is being produced, streamed, and consumed at an immense rate, and that rate is projected to grow exponentially in the future. In particular, JSON is the most widely used data format across streaming technologies and workloads. As applications, websites, and machines increasingly adopt data streaming technologies such as Apache Kafka and Amazon Kinesis Data […]

Streaming ETL jobs in AWS Glue can consume data from streaming sources such as Amazon Kinesis and Apache Kafka, clean and transform those data streams in-flight, as well as continuously load the results into Amazon Simple Storage Service (Amazon S3) data lakes, data warehouses, or other data stores. The always-on nature of streaming jobs poses […]

The volume of data being generated globally is growing at an ever-increasing pace. Data is generated to support an increasing number of use cases, such as IoT, advertisement, gaming, security monitoring, machine learning (ML), and more. The growth of these use cases drives both volume and velocity of streaming data and requires companies to capture, […]

Many customers are modernizing their data architecture using Amazon Redshift to enable access to all their data from a central data location. They are looking for a simpler, scalable, and centralized way to define and enforce access policies on their data lakes on Amazon Simple Storage Service (Amazon S3). They want access policies to allow […]

Data lakes have become very popular with organizations that want a centralized repository that allows you to store all your structured data and unstructured data at any scale. Because data is stored as is, there is no need to convert it to a predefined schema in advance. When you have new business use cases, you […]

Real-time insight is critical when it comes to building trading strategies. Any delay in data insight can cost lot of money to the traders. Often, you need to look at historical market trends to predict future trading pattern and make the right bid. More the historical data you analyze, better trading prediction you get. Back […]

Businesses collect more and more data every day to drive processes like decision-making, reporting, and machine learning (ML). Before cleaning and transforming your data, you need to determine whether it’s fit for use. Incorrect, missing, or malformed data can have large impacts on downstream analytics and ML processes. Performing data quality checks helps identify issues […]