This guest blog was written by Mac Noland of phData.
This was previously posted on the phData blog site on February 12, 2019.
As distributed data platforms like Hadoop and cloud grow in adoption, there increasingly needs to be a more distributed approach to business intelligence (BI) and visual analytics. Traditional BI tools no longer scale to the increased business needs.
At phData we continue to run into traditional BI tools failing to adapt to the increasing data firehose and business needs to analyze the volume of data necessary for data-driven decision making. Moreover, the infrastructure needed for these tools continues to grow vertically into expensive solutions. What is needed is a platform that takes advantages of distributed compute and storage technologies and embeds itself into the ecosystem instead of living off to the side.
Arcadia Data, the only pure-play distributed BI and visual analytics tool, fills this business need. Arcadia’s native approach to run on the same Hadoop and Cloud infrastructure simplifies the BI deployment model and eliminates the needs to stand up and support dedicated BI servers.
Arcadia’s visualization server runs on an edge node and spreads compute across the cluster using the Arcadia in-cluster analytics engine that is easily deployed via standard Hadoop and Cloud service management tools.
No longer do end users need to install locally deployed client tools to develop solutions. All Arcadia development is done on the centrally housed Arcadia server, which again runs on Hadoop and Cloud edge nodes. At phData we are seeing businesses save $100,000+ in savings in reduced client license costs and operational overhead.
Moreover, Arcadia’s ability to scale its queries across the cluster and utilize analytical views has allowed phData customers to do analytics on data sets too large to have ever worked with before. And, Arcadia’s approach allows customers to avoid moving data out of Hadoop and Cloud to do BI and visual analytics on mid-sized datasets. phData has seen customers save $100,000+ in data engineering costs to extract data and denormalize it on vertically scaled enterprise compute and storage solutions. Lastly, with Arcadia’s native support for Hadoop and Cloud security, no longer is there the need to support and maintain yet another security model.
In closing, Arcadia’s Hadoop and Cloud native approach has enabled our customers at phData to scale their BI and visual analytics with their data, rather than spending millions of dollars to scale up traditional tools.