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What is SigView?

Unlock comprehensive access to intricate data for effortless analysis of vast datasets and obtain real-time reporting in just seconds! Sigview, a user-friendly data analytics solution from Sigmoid, streamlines the exploratory data analysis process and is built on the robust Apache Spark framework, enabling users to explore large volumes of data almost instantaneously. With around 30,000 users globally utilizing this tool to analyze billions of ad impressions, Sigview is meticulously crafted to deliver prompt access to both programmatic and non-programmatic data while producing real-time reports. Whether your goal is to boost ad campaign effectiveness, discover new inventory, or investigate revenue opportunities in a dynamic market, Sigview stands out as the premier platform for all your reporting needs. Its ability to effortlessly connect with diverse data sources, such as DFP, Pixel Servers, and audience viewability partners, allows for the integration of data in any format and from various locations, all while maintaining data latency under 15 minutes. This feature empowers users to make rapid, informed decisions and adjust to the evolving business environment with assurance. Furthermore, the intuitive interface makes it accessible for users of all skill levels, ensuring that everyone can harness the power of data analytics to drive their strategies forward.

What is Apache Spark?

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.

Media

Media

Integrations Supported

Integrations Supported

Alibaba Log Service
Alteryx
Apache Iceberg
DQOps
Databricks
Deeplearning4j
ELCA Smart Data Lake Builder
Genesis Computing
Inferyx
LogIsland
Mage Static Data Masking
Sematext Cloud
Snowflake CoCo
Spark NLP
Stackable
StarRocks
TeamStation
Yottamine

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Sigmoid

Date Founded

2013

Company Location

United States

Company Website

www.sigmoid.com/solutions/sigview/

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

spark.apache.org

Categories and Features

Big Data

Collaboration
Data Cleansing
Data Visualization
High Volume Processing

Categories and Features

Big Data

Not specified

Data Analysis

Not specified

Data Modeling

Not specified

Query Engines

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation

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