Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility, and to improve system resilience and reliability. One common use of data replication is for disaster recovery, to ensure that an accurate backup always exists in case of a catastrophe, hardware failure, or a system breach where data is compromised. Having a replica can also make data access faster, especially in organizations with many locations. Users in Asia or Europe may experience latency when reading data in North American data centers. Putting a replica of the data closer to the user can improve access times and balance the network load. Replicated data can also improve and optimize server performance. When businesses run multiple replicas on multiple servers, users can access data faster. Additionally, by directing all read operations to a replica, administrators can save processing cycles on the primary server for more resource-intensive write operations.

When it comes to data analytics, data replication has yet another meaning. Data-driven organizations replicate data from multiple sources into data warehouses, where they use them to power business intelligence (BI) tools.

Benefits of data replication

By making data available on multiple hosts or data centers, data replication facilitates the large-scale sharing of data among systems and distributes the network load among multisite systems. Organizations can expect to see benefits including:

• Improved reliability and availability: If one system goes down due to faulty hardware, malware attack, or another problem, the data can be accessed from a different site.
• Improved network performance: Having the same data in multiple locations can lower data access latency, since required data can be retrieved closer to where the transaction is executing.
• Increased data analytics support: Replicating data to a data warehouse empowers distributed analytics teams to work on common projects for business intelligence.
• Improved test system performance: Data replication facilitates the distribution and synchronization of data for test systems that demand fast data accessibility.

What is a data pipeline?

A data pipeline is a set of tools and activities for moving data from one system with its method of data storage and processing to another system in which it can be stored and managed differently. Moreover, pipelines allow for automatically getting information from many disparate sources, then transforming and consolidating it in one high-performing data storage.

With Hexalytics Self-Service tool, the power to solve is a simple drag-and-drop experience. We equip business users for self-service success by developing a complete engagement plan, help them to solve any analytic use case, regardless of the skillset, and see outcomes in record time.

Prepare, blend, and analyze data

See how easy it is to prep, blend, and analyze data from any data source. Automate reporting, predictive, geospatial analytics, and more.

Low-code, no-code modeling & data science

Create advanced predictive and analytic models using dozens of no-code, low-code building blocks. Experience “expert mode” using integrated R and Python tools.

Since 1980s, data warehouse concept has born and it was developed to help transition data from merely powering operations to fueling decision support systems that reveal business intelligence. A data warehouse is a large collection of business data used to help an organization make decisions. The large amount of data in data warehouses comes from different places such as internal applications such as marketing, sales, and finance; customer-facing apps; and external partner systems, among others. On a technical level, a data warehouse pulls data periodically (such as daily, weekly, monthly) from those apps and systems; then, the data goes through formatting and import processes to match the data already in the warehouse. The data warehouse stores this processed data so it’s ready for decision makers to access. How frequently data pulls occur, or how data is formatted, etc., will vary depending on the needs of your organization.

The future of the data warehouse:

Move to the cloud As businesses make the move to the cloud, so too do their databases and data warehousing tools. Hexalytics cloud solution offers many advantages: flexibility, collaboration, and accessibility from anywhere, anytime. Popular tools like Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, Google BigQuery, and have all offered businesses simple ways to warehouse and analyze your cloud data.

The cloud model lowers the barriers to entry — especially cost, complexity, and lengthy time-to-value — that have traditionally limited the adoption and successful use of data warehousing technology. It permits an organization to scale up or scale down — to turn on or turn off — data warehouse capacity as needed. Plus, it’s fast and easy to get started with a cloud data warehouse. Doing so requires neither a huge up-front investment nor a time-consuming (and no less costly) deployment process.

Hexalytics cloud data warehouse architecture largely eliminates the risks endemic to the on-premises data warehouse paradigm. You don’t have to budget for and procure hardware and software. You don’t have to set aside a budget line item for annual maintenance and support. In the cloud, the cost considerations that have traditionally preoccupied data warehouse teams — budgeting for planned and unplanned system upgrades — go away.

Information mart usually has the data from the Data Vault and the Business Vault. Data source view needs to be created which is used to access the relational database model in the information mart.

In most cases, information marts don’t use foreign key references. Therefore, Analysis Services offers the option to create logical relationships from the metadata of the tables found in the information mart. The relationships are found based on the column and table names.

Hexalytics professionals know how to deal with common issues and how to create common Business Vault entities. We specialized in loading slowly changing dimensions, fact tables and aggregated fact tables, and how to take advantage of point-in-time and bridge tables when providing the information mart. We know a lot of tips and tricks for providing temporal dimensions and performing data cleansing using PIT tables, also how to deal with reference data when loading the dimensional information mart and using hash keys in the dimensional model.