Layanan Big Data & Analitik
Manfaatkan Kekuatan Data dengan Solusi Terkini Kami. Dapatkan wawasan berharga, dorong pertumbuhan, dan buat keputusan berbasis data mulai hari ini.
“Without big data, you are blind and deaf and in the middle of a freeway.”
Geoffrey Moore
Management consultant and author
Manfaat Big Data
Mitra Kami dalam Proyek Big Data
Kami bekerja sama dengan mitra untuk mengubah data menjadi wawasan berharga.
Point of Sales Martabak Orins
Optimize database performance using complex indexing
HSIS & HNI e-Commerce
Implementing a reliable log monitoring using non-relational database such as MongoDB
Layanan Big Data dan Analitik Kami
Kami memahami pentingnya data dan dampaknya terhadap bisnis Anda. Karena itu, kami menyediakan rangkaian lengkap layanan big data untuk membantu Anda memanfaatkan kekuatan data dan mengubahnya menjadi wawasan yang dapat ditindaklanjuti.
Kami memahami pentingnya data dan dampaknya terhadap bisnis Anda. Karena itu, kami menyediakan rangkaian lengkap layanan big data untuk membantu Anda memanfaatkan kekuatan data dan mengubahnya menjadi wawasan yang dapat ditindaklanjuti.
Database Performance Optimization
Improving the performance of a database, typically by identifying and resolving bottlenecks, or areas where the database is underperforming.
Scope of Work:
- Indexing
- Reducing disk I/O
- Increasing the scalability of the database
- Query optimization
- Physical and infrastructure optimization
Big Data Processing
The collection, storage, management, and analysis of extremely large and complex data sets.
Scope of Work:
- Collection, storage, management, and analysis of extremely large and complex data sets.
- Processing data from a variety of sources, such as social media, sensor data, and transactional data. The data can be structured, semi-structured, or unstructured.
Data Warehouse / Data Lake Implementation
This process involves several steps: data collection and ingestion, data storage, data transformation, data modeling, data access and data governance.
Scope of Work:
- Data warehouse is used for reporting and OLAP (Online Analytical Processing) purposes,
- Data lake is used for OLAP and OLTP (Online Transactional Processing), and support different data processing engines such as batch, real-time, and interactive.
Self Service Dashboard
Type of business intelligence tool that allows users to access and analyze data without the need for technical assistance.
Scope of Work:
- Visualize a range of features, such as drag-and-drop interfaces, pre-built templates, and data visualization options.
- It also allow users to connect to various data sources, such as databases and spreadsheets, and perform data analysis and data mining.
Data Integration
Bringing together data from various sources, such as transactional systems, social media, and sensor data, and making it available for reporting and analysis.
Scope of Work:
- Data extraction: Collecting data from various sources and storing it in a centralized repository, such as a data warehouse or data lake.
- Data transformation: Cleaning, transforming, and integrating the data to fit the data warehouse or data lake schema. This can include tasks such as data mapping, data normalization, and data validation.
- Data loading: Loading the transformed data into the data warehouse or data lake for reporting and analysis.
- Data quality: Ensuring that the data is accurate, complete, and consistent, and identifying and resolving any data quality issues.
Improving the performance of a database, typically by identifying and resolving bottlenecks, or areas where the database is underperforming.
Indexing
Reducing disk I/O
Increasing the scalability of the database
Query optimization
Physical and infrastructure optimizationThe collection, storage, management, and analysis of extremely large and complex data sets.
Collection, storage, management, and analysis of extremely large and complex data sets.
Processing data from a variety of sources, such as social media, sensor data, and transactional data. The data can be structured, semi-structured, or unstructured.This process involves several steps: data collection and ingestion, data storage, data transformation, data modeling, data access and data governance.
Data warehouse is used for reporting and OLAP (Online Analytical Processing) purposes,
Data lake is used for OLAP and OLTP (Online Transactional Processing), and support different data processing engines such as batch, real-time, and interactive.Type of business intelligence tool that allows users to access and analyze data without the need for technical assistance.
Visualize a range of features, such as drag-and-drop interfaces, pre-built templates, and data visualization options.
It also allow users to connect to various data sources, such as databases and spreadsheets, and perform data analysis and data mining.Bringing together data from various sources, such as transactional systems, social media, and sensor data, and making it available for reporting and analysis.
Data extraction: Collecting data from various sources and storing it in a centralized repository, such as a data warehouse or data lake.
Data transformation: Cleaning, transforming, and integrating the data to fit the data warehouse or data lake schema. This can include tasks such as data mapping, data normalization, and data validation.
Data loading: Loading the transformed data into the data warehouse or data lake for reporting and analysis.
Data quality: Ensuring that the data is accurate, complete, and consistent, and identifying and resolving any data quality issues.Perangkat Teknologi
Tools
Programming Language
Database
Technology Stack
Tools
Programming Language
Database
Insights
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