Data analytics for your business and digital operations
Data analytics for your business and digital operations
Big data
A large and complex set of data that traditional data processing applications cannot efficiently handle. This dataset may include structured, semi-structured, and unstructured data from various sources.
Three
main pillars
Identification of the problem
and setting requirements
Through consultative sessions, we identify the problem(s) that need to be solved using Big Data. Once we have a clear understanding of the objectives, we determine the technical and business requirements necessary to approach a Big Data project (storage, processing, and analysis).
Data identification and collection
We implement continuous data collection processes using technologies like ETL, Kafka, among others, to query various data sources: databases, sensors, social media, server logs, etc.
At the storage level, we use tools that can handle large volumes and diverse data .(e.g., Hadoop HDFS, Cassandra, MongoDB) to create data lakes for future analysis.
Data identification and collection
At Wiglabs, we implement batch and real-time data processing (when required) to handle large amounts of data at regular intervals using tools such as Apache and Spark.
Regarding analysis, we leverage machine learning technologies and algorithms to implement advanced models, predict future trends, and suggest actions.
Visualization and security
Data visualization
With our team of experts, we design data models that represent the relationships between different datasets, providing processed information for the creation of reports and interactive dashboards with clear and concise information for end users.
Data security
We ensure from the development stage that data is protected against unauthorized access through encryption, authentication, and authorization.
Maintenance and optimization
monitoring systems to supervise the performance and health of the Big Data system.
We scale horizontally to handle growing volumes of data.
We continually update the infrastructure and analysis algorithms to improve performance.