April 18, 2023
In today's rapidly evolving technological landscape, digital transformation has become a critical business imperative. The key drivers and objectives of transformation efforts are to enhance decision-making, maximize efficiencies and create compelling customer experiences. To achieve these goals, organizations require an Analytical Data Store with modern data storage, analytical capabilities, high scalability, and faster querying speed.
Data is growing at exponentially high speeds. The total amount of data generated by 2025 is set to accelerate further to 175 zettabytes. The global big data market is also expected to reach $273.4 billion by 2026, highlighting the growing importance of data analytics in today's business landscape. As data becomes more critical for organizations, it is necessary to store, manage, and analyze data effectively to gain valuable insights that help drive business success.
Stronger decisioning capabilities: To remain competitive and agile in an increasingly crowded marketplace, identify opportunities, optimize processes and pivot quickly while driving long-term business success and sustainability.
Improved operational efficiency: To optimize processes and resources, and create a competitive business model that can adapt to changing market conditions and customer needs.
Enhanced customer experience: To deliver superior digital experiences to customers that simplify access to information whenever needed and provide seamless interfacing abilities across multiple channels.
Modern data store
Faster querying speed
Modern data store vs. traditional data store
Modern data stores enable businesses to store and process both structured and unstructured data in a flexible and efficient manner. Such data stores can analyze large volumes of data to extract valuable insights, while their high scalability can help handle massive data volumes, and maintain performance and productivity at optimum. With the fast querying speeds of modern data stores, businesses get access to insights quickly to make informed decisions.
However, traditional data stores are limited in terms of scalability and flexibility. As data volumes increase, traditional data stores struggle to scale, leading to decreased performance and productivity.
The MongoDB Analytical Data Store, powered by Exafluence, stores both structured and unstructured data, performs faster, and accurate data analysis, and scales efficiently, to unearth valuable insights that are difficult to identify using traditional data stores. Unlike traditional data stores, the MongoDB Analytical Data Store provides optimal performance by leveraging its distributed architecture, automatic data sharding, and horizontal scaling.
Benefits of using MongoDB analytical data store
The key benefit of MongoDB Analytical Data Store is its ability to handle large volumes of data without compromising on performance. MongoDB's distributed architecture allows data to be partitioned across multiple servers, providing efficient data processing and faster query response times. Additionally, MongoDB's automatic data sharding enables data to be partitioned and distributed across multiple nodes, ensuring that data is evenly distributed across the cluster.
The Data Store’s ability to store both structured and unstructured data is particularly important for organizations that deal with large data volumes from diverse sources. MongoDB can also handle a variety of data types, including text, geospatial data, time-series data, and binary data. Other benefits include -
Flexible document schemas: Schema-less database gives the flexibility and freedom to store data of different types
Easy horizontal scale-out with sharding: Shards high-throughput collections across multiple clusters to sustain performance and scale horizontally
Powerful querying and analytics: Simplifies access to data and performs complex analytics pipelines
High-performance capabilities: Enables faster querying and returns all the necessary information in a single call to the database
Case study 1: Utility sector | AI platform for a network of IoT device
- The client had over 300K+ connected IoT sensors across their city and needed detailed information on the sensor's communication data to improve decision-making and optimize field visits
- The client wanted to identify cluster-wise unconnected devices, sensor patterns for choosing the gateways, sensors connected in different spreading factors and regions, and the capacity of the gateways by using machine-learning models
- Exafluence customized the IoT Devices AI Platform for the client, which stored the sensor data on MongoDB, delivered insights on sensor connectivity and behavior, and enabled faster decision-making
- The platform helped monitor the performance, availability, and health of a network of IoT devices through a single, all-in-one platform powered by AI
Case study 2: Healthcare | Modernizing technology architecture
- The client's data volume and complexity had increased multifold, affecting the performance of applications, databases, and reporting tools
- Reports generated with the existing architecture were based on 10-day-old stale data, which was a major concern for the client
- Exafluence modernized the client's technology architecture and made necessary programming changes, helping the client generate reports with near real-time data
- Leveraging a Spark-based solution, Exafluence completed the migration from MS SQL Server to MongoDB Atlas, creating an Analytical Data Store on the Atlas platform
- Over 850 million records processed with speed and accuracy
- Load completed within just two days
In today's hyper-competitive business environment, the ability to harness the power of data is essential for success. That's where MongoDB Analytical Data Store comes in. The game-changing solution empowers businesses to manage and analyze massive amounts of data with ease. With MongoDB's cutting-edge analytics, unmatched scalability, and lightning-fast querying abilities, businesses can unlock valuable insights, make informed decisions, and take their operational efficiency up by a notch.