Key components of a data analytics architecture
Zennemis – Key components of a data analytics architecture. An system for data analytics that has been carefully thought out and is well put together can get rid of these problems. Businesses look at their business strategies to figure out what data-driven ideas they need. The who, what, how, and why of the analytics process are all spelled out in the data analytics design.
For instance, if giving employees more freedom is important, then workers shouldn’t rely on data experts for every question. A data architecture would spell out the kinds of analytics tools that different types of choices will need, such as dashboards and software that runs on SQL.
A well-thought-out company business strategy guides the overall data architecture, which in turn shapes the information infrastructure. The data architecture includes data storage, data ingestion, and data analysis. It will become more in line with policy over time in how the company stores, handles, and uses its huge amounts of data.
1. Data storage
Data analytics depends on where companies put their data because it’s faster to get and analyze data that is close to the user. For this reason, businesses got rid of their separate relational databases and replaced them with data warehouses and then legacy data lake systems.
These systems sucked huge amounts of data into one place where data users could handle it and get to it, with the help of data engineers. As was already said, these single-piece systems didn’t replace all old systems, and they were expensive to keep up.
An design for data analytics that uses Starburst is built around a modern data lake. Many types of data can stay where they belong with Starburst’s single point of entry, which is made possible by connections to more than 50 enterprise data sources. The data lake is still where the company’s most important data is stored, but data teams don’t have to worry about getting every data point that could be important.
Starburst’s data lake analytics tool makes it easier to keep data lakes diverse, accurate, up-to-date, and of high quality.
2. Data Ingesting, Processing and Transformation
Supporting business analytics from single-platform data storage needs a huge investment in building and maintaining data pipelines. For each request, engineers have to make an extract, transform, and load (ETL) process. Also, they have to keep a close eye on data sources to make sure that any changes don’t mess up these processes.
A modern design for data analytics makes an abstraction layer that virtualizes the company’s data architecture. Authorized users don’t need ETL pipelines to look at data from any source through a single point of entry like Starburst.
A lot of projects that used to need the time and resources of data engineers will never need one again. Large projects may still need pipelines, but this ETL-free research phase will cut development times by a large amount.
3. Data Analysis and Exploration
Data in legacy data stores and data lakes is hard to see. Things are hard to find. It varies from source to source in terms of organization, format, and quality. So, people who make decisions have to wait for data teams to clean and handle data before analysts can start working on it.
Starburst’s data lake analytics tool hides the complexity of modern data architectures from people who use data and the engineers who work with them. Anyone who works with data can connect to any storage layer, file format, or table type that they need. Starburst also gives you the control and visibility that best-in-class data governance processes need.
Data analytics architecture: Real-world use case and healthcare case study
Optum, a healthcare company, shows how to use Starburst’s data lake analytics tool to build a modern data architecture.
1. Data sources
There are many SAS, Microsoft SQL Server, Teradata, and Postgres systems in Optum’s infrastructure, as well as a Hadoop data lake that is petabyte-sized.
With Starburst connectors, Optum was able to build a virtual data design that brought together all of its data sources into a single system. For the company’s insights to work, data no longer needs to be copied or moved between silos.
Starburst also separates storage from compute, which lets Optum adjust resources based on demand. This leads to a 30% drop in resource usage.
2. Data processing
Optum’s data team had to make ETL pipelines to copy and process data when analysts wanted data from more than one source. This process was too rigid and slow for the 10,000 users who need results in seconds. It cost a lot of money and took a long time.
Starburst lets users directly explore any data source with the SQL tools they already know how to use. There’s no need for pipes. Analysts at Optum can get answers from ad hoc queries up to ten times faster than before. This cuts down on the time it takes for them to gain insight.
3. Results in real time while keeping PHI safe
As a health care company, Optum’s job is to keep the personal health information (PHI) in its processes safe. At the same time, the business needs to make data easy to find so that ideas can be found that improve both patient outcomes and business performance.
Starburst provides Optum’s authorized users with the access they need through a single point of entry. This access enables them to make a significant impact on decision-making and operations. Optum reports that measures for keeping customers satisfied have improved due to this enhanced access. The company believes that faster time to insight will lead to substantial savings, potentially millions of dollars.
Starburst helps Optum get things done, and our virtualized data layer helps protect Optum’s PHI at the same time. Access can be managed from one place with Starburst Enterprise. Access can be limited by table, column, and row with fine-grained, role-based access rules. This makes sure that only authorized users can see sensitive data.
Conclusion: Key components of a data analytics architecture
In conclusion, a well-structured data analytics architecture is crucial for businesses aiming to optimize their data handling. By carefully considering data storage, companies can improve access and speed for better insights. Moreover, modernizing data ingestion and processing methods reduces reliance on complex ETL pipelines, making data analysis faster and more efficient. The use of advanced tools like Starburst allows for seamless integration across various data sources without compromising quality or security. Additionally, real-time data processing, combined with strict access controls, ensures data remains both accessible and protected, fostering enhanced decision-making and operational performance.
