Choosing the appropriate data model and understanding how to implement it is a key decision to make early in the agile development process, as this affects the app’s functionality. First, we must see how the data model fits the app requirements and assumptions in order to best structure the data.

Data is structured by considering the interrelationship of data items and the efforts or complexities of the app’s code. The type of data relationship or purpose determines the data model or mixed model. The most common data model types are relational (SQL) or non-relational (NoSQL) datastores, documents, and graph-based models.

We must define the data, the layers, and how the data model will represent this—also, we must answer questions relating to how we will store the data and how the mobile application will access the data. Data models must be suitable for the individual use case and be capable of adapting the app to changing requirements.

Building data relationships, code access paths, and technical details on how to structure and query data is critical for success. Contact an app consulting team for more information on how this technology can be utilized to improve your company’s functionality and effectiveness in the marketplace.

Mobile apps require code to build a supporting data system with a data model implementation that synchronizes interactions with data formats (JSON, XML) and schemas for encoding data, search indexing, caching, etc. There is plenty of development work to ensure that the mobile app performs to satisfy the expected user experience.

An app development engineer completes an evaluation with the client and gives special treatment to each case to deliver performance and develop a system considering scalability and long-term maintenance.

Building data-intensive apps requires various components to perform the desired functions while considering high data volume, loading parameters, latency, and response times. Sometimes components fail, and the system must be tolerant (fault-tolerant system) to handle faults with established continuous testing and other techniques. DevOps teams continuously monitor and track failures with ready strategies for restoring services.

At Krasamo, we practice development in sandbox environments, manual and automated testing, monitoring, and architectures that can handle appropriate load levels. Also, distributing the loads can be done with elastic computing resources or manual scaling.

There are many issues about data volume that can determine the scalability of the system’s architecture. We design systems to handle specific data throughput loads and leave the options open to scale with their corresponding assumptions or anticipate new requirements. Mobile app development teams must keep in mind simplicity and maintenance recommendations for easy operations and future evolution.

Simplifying the system can be done by implementing abstractions of components and hiding their complexities to reuse them later, improving the quality of the data system and its ability to adapt to new requirements.

Building a robust data architecture is necessary for synchronizing data from several sources and applications to provide a seamless experience for users.

Integrating mobile apps to the architecture of data systems is fundamental, as is keeping a clean data model and simple app code. Data models allow searching data using frameworks or query languages such as SQL. Then, developers build (code) and query access paths to retrieve data.

Databases can be configured to replicate data using a mix of asynchronous or synchronous communication in order to have copies (replicas) of the data in the networked machines or nodes and process changes continuously. Krasamo can help choose the best approach for your use case during the app development planning phase.

By coding or picking a built-in database replication approach (replication mode or algorithm), we create a stream for replicating data consistently across nodes. A common approach is using the leader-based replication form, in which data is written only in the leader database and replicated to the other databases (followers).

Data change streams use a log system to log statements (write requests), track any changes, and catch up or restore from their last transaction in case of node failures (crashes). The log system helps build replicas of data structures from the storage engine.

For more complex systems with multiple data centers or offline operations, you can use the multi-leader or leaderless replication methods as options for specific use cases. Our app developers can give you a good assessment of process consistency, conflict resolution, and other technical design choices.

Krasamo’s app developers will consider your app-specific requirements and replication characteristics in order to create custom code to execute specific transactions in the database and send data to other applications.

Data systems can manage datasets using partitions to distribute loads or execute queries in several nodes.

App developers can implement several partition schemes and combine them with replication schemes. Also, other technical operations such as rebalance partitions and routing queries can be considered.

System administrators set up approaches and handle leader–follower relationships and failure issues (failovers), reconfigure new leaders, shut down nodes, and manage network issues, concurrency issues, conflict resolution, and coordination with other databases.

Some apps with complex access patterns may require coding transactions (abstraction layers) with logic of database reads and writes to access the database.

App development efforts focus on designing reliable and fault-tolerant systems, as well as contemplating partial failures and expected system behaviors (assumptions).

A challenge for developers is the configuration of network topologies and how the system reacts to and recovers from failures, application crashes, or detecting faults.

Developers routinely make decisions and trade-offs to overcome problems and know how to handle expected loads in network queuing, including congestions and delays, timeouts, and variability of delays.

App development requires defining the system model and having clear assumptions about the system behavior and how to optimize resources.

The system model is defined by considering assumptions about the timing and how nodes fail in specific situations—solving problems logically while managing app implementation and partial failures.