What's New: Notification Channel Feature
This video provides an overview of the notification channel feature and was recorded in the 4.7.8.0 release. #unraveldata #unraveldemo
Analytics
Enable self service and simplify management of your modern data stack using Unravel
Enable self service and simplify management of your modern data stack using Unravel Data-forward organizations struggle to hire and retain top talent to architect, build, and operate data applications and pipelines required for rapid growth. See how data observability tools enable data teams to more efficiently achieve data performance, cost, and quality SLAs. Learn how innovative companies such as Maersk, HSBC, and FirstRand Bank use Unravel to simplify and accelerate data observability at scale.
Cloudera DataFlow Designer: Kafka to Iceberg in Cloudera Data Warehouse
Learn how to use the new Cloudera DataFlow Designer to build a streaming data pipeline that ingests events from Kafka into Iceberg tables.
Data lake vs. data mesh: Which one is right for you?
What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.
Top 10 Data Integration Tools to Try in 2023
In the modern era, effective decision-making is powered by data-generated insights. This data is typically spread across various sources and applications. Companies can analyze each data source separately, but it won't be an efficient option since it doesn't bring the whole representation of data comprehension. And that's where data integration tools come in.
Materialized Views in SQL Stream Builder
Cloudera SQL Stream Builder (SSB) gives the power of a unified stream processing engine to non-technical users so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. There are many ways to distribute the results of SSB’s continuous queries to embed actionable insights into business processes.
Automating your data for better decision-making
Instead of relying on inaccurate, messy and low-quality data, business leaders can instead drive innovation forward by turning to automated data management, creating a clean and steady data flow.
API vs. Data Integration
Businesses like yours need to connect applications and data sources to better serve customers, automate processes, and improve decision-making.
Data modeling best practices for data and analytics engineers
Recently, I published an article on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.
Is Self-Service BI a Hollow Promise or Crucial Capability?
As technology advances and digitization takes over, there is an expectation that our lives will be more simple. ‘Self-service’ capabilities like Self-Service BI are the manifestation of this expectation within many technologies. For most, ease of use is no longer enough. Now tools must be simple to use, and flexible enough to cater to a wide range of skills and intricacy of analysis.