Making sense of the giant volumes of data you are collecting is key to making quick decisions and staying ahead of the competition. Specifically, understanding how different variables correlate together, the timing and reoccurrence of historical trends, and executing real-time analysis of market dynamics all help in making the right business decisions.
What has helped greatly are business intelligence (BI) visualizations, which have evolved dramatically to deliver dynamic charts and graphs, heatmaps, dashboards, histograms, using modern visualization platforms such as Amazon Web Services (AWS) Athena, and other visualizing platforms.
Collecting data, however, is a complicated business, and to meet the need organizations are increasingly using multiple private and public clouds. A multi-cloud approach affords far greater flexibility, reuse opportunity, and extended reach to choose the best-of-breed services.
A 2019 Gartner survey found that 81% of public cloud users were working with two or more cloud providers. During the COVID-19 crisis, the move toward hybrid multi-cloud architectures further accelerated, with more users looking to migrate data to the cloud.
But this approach has also resulted in data silos, i.e., data being distributed across different clouds or cloud regions. In most situations, the approach of copying the data into a centralized, consolidated repository is not a feasible option. The sheer high volumes can make simple data replication impractical. Also, in different cloud regions, data privacy regulations might prohibit the copying of data out of local jurisdiction.
Completely migrating data to the cloud is the holy grail of enterprise analytics, as it provides companies with the foundation for insightful visualization. It is an expensive undertaking, however, and not all companies are able to execute this at scale. That’s because the impact on ongoing operations, business continuity, existing license arrangements, timing, and complexity prevent many customers from embarking on holistic data migrations.
So how can you do great BI visualization without a full-on migration? The answer lies in data meshing, which means staging data that enables data analysts, engineers, and data scientists to execute SQL queries across data stored in the relational, non-relational, object, and custom data sources.
Our clients increasingly like to take advantage of an interim step allowing most if not all the benefits of great visualizations tools without a complete migration project. At Trianz we have developed an accelerated solution with Athena Federated Query (AFQ) extensions through which companies can stage data from different data platforms. You can submit a single Structured Query Language (SQL) query and analyze data from multiple sources running on-prem or hosted on the cloud. This allows you to leverage great visualization tools without completing a holistic data migration to a single platform.
When the enterprise needs to view data from multiple sources, Trianz’s AFQ Extensions helps by querying and combining data from Teradata, Google BigQuery, and SAP HANA databases with AWS Storage Services. This eliminates costly data movements and enables the querying of data wherever it resides.
With Trianz’s AFQ extensions, you can:
Connect and govern multiple data sources (such as Teradata, BigQuery, SAP HANA, S3, Redshift, and others) with zero data movement.
Build data models and powerful dashboards across multiple sources while hiding the complexity of backend technologies from the end-user.
Use Athena Queries on any visualization tool.
Simplify your migration process by accessing different data sources with minimal disruption and cost.
Easily deploy on AWS Serverless Application Repository in your environment within minutes.
In short, using a method to stage data from multiple sources allows you to improve efficiency and reduce costs while still getting the benefits of an integrated approach to data management and leveraging BI visualization tools.
With zero infrastructure, straightforward implementation, and faster response times, AFQ extensions are an effective way to start your visualization journey immediately.
Cheaper to maintain than traditional integration tools, because physically replicating, moving, and storing data multiple times is expensive
Faster data management, rather than having to wait hours or even days for your results with traditional data integration methods
Enhanced performance with low network latency, as Trianz AFQ connects directly to the source and provides actionable insight in real-time
Complements traditional data warehousing, as the solution works with on-prem analytics providers like Teradata
Secure, reliable, easy, and fast access to data views while supporting multiple data formats
Faster time to market for any business data on Teradata, BigQuery, SAP HANA, and AWS data sources
Eliminates development efforts
Zero business disruption
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
The Cloud is the Key to Transformation Success… Transitioning your applications to the cloud is undeniably a critical factor to a successful digital transformation endeavor. It’s more than just a lift-and-shift, however. Let’s explore several things that you need to consider before migrating your applications to the cloud, including: Readiness of your application portfolio Where to begin – the right business case and migration strategy Technology requirements and considerationsExplore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore