WHY IT MATTERS: Digital Transformation

The 2022 edition of the Critical Capabilities for Analytics and Business Intelligence Platforms has the same 12 capabilities as the 2021 edition. However, the underlying questionnaire used to evaluate all 20 vendors was expanded, adding nine new subcriteria for a total of 79 questions.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

Few organizations take full advantage of data generated outside their walls. A well-structured plan for using external data can provide a competitive edge.

Looking at different ecommerce metrics is like gazing into the night sky: there are so many data points you can (and should) track that the information available feels...infinite. Connecting and integrating data across different channels and platforms can be a full-time job, and as a result, teams are constantly playing catch-up with all of the numbers and data points they’re tracking across spreadsheets and dashboards. Enter an ecommerce dashboard.

This frequently-updated page contains real-life examples and diagrams from Upsolver customers who are using AWS data lakes. While the Upsolver platform itself is a key component in all of these architectures, we hope even non-customers can learn from these examples when it comes to the use cases, considerations, and design patterns that go into a typical cloud data lake.

To succeed with data and analytics initiatives, enterprises need a comprehensive view of critical technology capabilities. We believe these Hype Cycles draw a holistic view of technologies — including the new addition of lakehouse architecture as a trigger, which can make data and analytics programs modern, broadly applied, richer in functionality and successful.

Get the report and gain insight into:
-Changes in this Hype Cycle — including the addition of Lakehouse
-The current state of data management maturity
-The Priority Matrix

In this article, we’ll present seven of the key best practices you should adhere to when designing, implementing and operationalizing your data lake. These are things that will mostly be automatically implemented if you’re using Upsolver, but if you’re building your data lake ‘manually’ you’ll need to ensure they’re done correctly if you want to get the most value out of your data lake architecture.
While we will mostly be referring to Amazon S3 as the storage layer, these best practices would also apply for a data lake built on a different cloud or using Hadoop on-premise.

Data lakes are key to scalable, open data architectures – but they can pose challenges to data engineering teams. Efficient data integration is the difference between a bunch of log files sitting in Amazon S3 or Azure Storage and a high-performance data lake that provides real value to analytics, data science and engineering teams.
In this guide we will cover some of the essential best practices and guidelines on how to make your data lake analytics-ready using cloud data processing and integration tools.

While CDP vendors may claim to do everything, the fact is they can’t.

We need to be able to fit new concepts and components neatly into existing architecture—to adapt to changes without disruption.

At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush.

But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.

Within a matter of hours of each day beginning, we ingest tens of billions of records from online sources to HDFS, aggregated across 50+ offline data flows, and visualize 40+ metrics across our business lines to support a dashboard providing company leaders with timely and critical business insights. Dubbed the Daily Executive Dashboard (DED), this dashboard contains critical growth, engagement, and bookings metrics that indicate the health of LinkedIn’s business. In this blog post, we’ll spotlight the challenges to DED’s delivery and performance posed by the growing complexity of computing and aggregating these metrics, and how this dashboard transformed our approach to data pipelines across the company.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly.