Category: Analytics

Managing cloud costs has become a top, strategic priority for most IT executives. When considering how to manage cloud cost, there are several primary paths that can be taken to lower total cost of ownership (TCO), both technical and non-technical. In this post, experts from SMX and AWS discuss how leveraging data-driven analysis and tools can improve decisions that impact short- and long-term spend.

Data mesh is a decentralized approach to data management which strives to develop the data platform from a technology-led and project-centric model into a paradigm about federated business-led and product-centric data, by design. Learn how AWS and Accenture are helping customers rapidly set up data mesh architecture on AWS leveraging the newly-announced Velocity platform. Explore how Velocity’s Data Mesh Fabric component can minimize time and effort to set up data mesh architecture on AWS.

AWS experts break down how you can build a multi-tenant data ingestion and processing engine using AWS services. We examine each component of this data pipeline and examine some of the key considerations that can influence how you approach designing a SaaS multi-tenant data ingestion process. We also explore how multi-tenant streaming data can be ingested, transformed, and stored using AWS services while ensuring there are constructs built in to the pipeline to ensure secure processing of the data.

Many organizations still are managing expensive single use case data environments, however, and IBM’s Modern Data Accelerators on AWS can help build modern implementation of a data fabric architecture, enabling customers to realize faster time to value. Learn how IBM’s Modern Data Accelerators can help build modern implementation of a data fabric architecture, which standardizes data integration across the enterprise.

Online analytical processing (OLAP) is a method of organizing datasets in a multi-dimensional format for quick analysis and provides deeper insights for decision-makers. Multi-dimension analysis is widely adopted by analysts, knowledge users, and power users for their decision support process. Learn how utilizing AWS analytic services and migration tools together with HCLTech frameworks to orchestrate OLAP solutions.

Many organizations have standardized or plan to standardize their unified data security governance on AWS Lake Formation. Some of these organizations are also leveraging Databricks and would like to create and manage data access policies for Databricks using AWS Lake Formation as well. Learn how Privacera’s integrated solution extends AWS Lake Formation source support to Databricks and provides data access policy authorship and maintenance from one safe and convenient location.

Many organizations are adopting data lakes to handle large volumes of data, and flexible pipelines to fit the needs of consuming services and teams (machine learning, business intelligence, and analytics). In this post, we’ll explore the modern data lake and how Fivetran can help accelerate time-to-value with Amazon S3 and Apache Iceberg. Fivetran offers pre-built connectors for 300+ data sources and employs ETL to land data in the warehouse or data lake.

Managing software development activities can be complex for enterprises, especially with the need for a single dashboard that provides insights into business, process, and people-centric metrics. Apexon’s COMPASS solution leverages machine learning to capture and integrate continuous data and intelligence across the software development lifecycle, providing a comprehensive view of resource performance and its impact on KPIs.

IBM’s watsonx is an artificial intelligence and data platform designed to scale and accelerate the impact of AI with trusted data. As a part of watsonx, IBM has launched watsonx.data, which enables customers to scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture. It provides querying, governance, and open data formats for easy data access and sharing. Customers can connect to their data within minutes, gain trusted insights quickly, and reduce data warehouse costs.

Organizations need to be able to carefully define, monitor, and manage who has access to specific pieces of data according to their policies and external regulations. Effective data governance ensures data is consistent and trustworthy without being misused. Learn how Privacera enables data access governance on Amazon Aurora, including tag-based access control policies, and how Privacera can be used to mask columns or filter by row.