Organizations across various industries are dealing with massive volumes of data that require extensive analysis and querying to help them better serve their customers. The sheer scale of this data can involve tens of thousands of metrics and dimensions, and data stores numbering in several petabytes.
To achieve real-time analytics, it usually takes a monumental effort to implement the query layer. Many organizations turn to open source alternatives like Apache Druid or Presto, along with data denormalization in separate pipelines, to ingest diverse data sources for multi-table queries.
However, this process demands significant resources and expertise, involving teams of engineers for implementation and maintenance, leading to time-consuming and resource-intensive efforts. Even minor schema changes can require days of effort, creating challenges for large organizations.
“Many people tend to give up on real-time analytics because of the organizational complexities they face when dealing with the software,” Sida Shen, product manager at CelerData, told The New Stack. “It’s the primary challenge they encounter, and it often leads them to dismiss the idea altogether.”
The Limits of Traditional Data Pipelines
Traditional pipelines lack flexibility, making it cumbersome to modify data models or pipelines. Each component adds complexity and increases the possibility of failure. Those components will likely lead to degradation in performance over time, not to mention the high operational costs.
Proper real-time analytics relies on various data transformations and data-cleaning processes. Additionally, pre-aggregation — which involves performing certain calculations in advance, such as denormalization — is used. (Denormalization means adding precomputed, redundant data to a relational database to improve its read performance.)
A “pipeline-free” solution addresses delays in data refreshing, minimizes latency, and reduces the complexity associated with denormalization and pre-aggregation steps, which often introduce time limits and delays in real-time analytics.
“The main advantage of going pipeline-free for real-time analytics is that it becomes much more accessible to a broader range of users, including those who may not be experienced engineers,
