Building a Data Stack on a Budget: An Affordable Guide to Data Management Sujeet Pillai January 17, 2023

Database management

A data stack is a combination of various tools and technologies that work together to manage, store, and analyze data. It typically consists of a data storage engine, an ingestion tool, an analytics engine, and BI visualization tools. In recent years, data stacks have become quite central to an organization’s operations and growth.

Data management is an essential part of any organization, and the way data is managed has evolved over the years. Data lakes and data warehouses were once only affordable by larger organizations. However, with the growth of the open-source data stack ecosystem, this has changed. The open-source data stack ecosystem has grown significantly in recent years, providing powerful alternatives for every layer of the stack. This has pushed the envelope for data stacks and reduced entry barriers for organizations to adopt a data stack.

One of the main reasons why data stacks have become more accessible is the availability of open-source alternatives. For every layer of the data stack, open-source alternatives are available that pack a serious punch in capability. These alternatives are often just as good, if not better, than their commercial counterparts. They also tend to be more flexible and customizable, which is essential for organizations that need to tailor their data stack to their specific needs.

Another reason why data stacks have become more accessible is the availability of cheap cloud resources. Cloud providers such as Amazon Web Services, Google Cloud, and Microsoft Azure provide low-cost options for organizations to set up and run their data stacks. This has made it possible for even smaller organizations to afford a data stack.

Organizations need to seriously consider this framework over a patchwork of point-to-point integrations. A patchwork of point-to-point integrations is often a result of an ad-hoc approach to data management. This approach is not only difficult to manage but also limits the organization’s ability to gain insights from its data. On the other hand, a data stack framework provides a more structured approach to data management, making it easier to manage, and providing the organization with the ability to gain insights from their data.

An Affordable Data Stack

One affordable data stack that organizations can consider is the following:

Storage Engine: Clickhouse

Clickhouse is a column-oriented database management system that can handle large data loads and has great query performance. It runs on commodity hardware and can be self-hosted using Docker. Clickhouse is designed to process large amounts of data, and its columnar storage model also makes query performance great.

Ingestion Engine: Airbyte

Airbyte is an open-source data integration platform that automates the ingestion of data sources and can be monitored and managed from a UI. It can also be self-hosted using Docker and has the ability to use Clickhouse as a sink. Airbyte automates the ingestion of data sources, making it easy to bring data into the data stack.

Analytics Engine: DBT

DBT is a powerful analytics engine that helps organize data models and processing. It’s built on SQL with jinja templating superpowers, making it accessible to a lot more people. DBT is a hero in the data lakes space, helping organizations organize their data models and processing. When building out an analytics process in DBT it’s quite helpful to use a conceptual framework to organize your models. I found this blog excellent to provide a great starting point.

Visualization Engine: Metabase

Metabase is a powerful visualization tool that makes it easy for organizations to gain insights from their data. It has lots of visualizations that cover most bases. The SQL query builder or ‘question wizard’ in Metabase is quite powerful for non-SQL experts to get answers from their data. It also has a self-hostable open-source version and can be set up in Docker relatively easily.

Infrastructure

For infrastructure, we recommend using Amazon Web Services. This stack can be deployed on a single m5.large instance for smaller-scale data and can be scaled up to a cluster configuration for larger data sets. Additionally, the different components of the stack can be separated into different servers for scaling. For example, if many Metabase users are accessing the data, it may be necessary to move Metabase onto its own server. Similarly, if ingestions are large, it may be necessary to move Airbyte onto its own server, and if storage and queries are large, it may be necessary to move Clickhouse into a cluster formation. This allows organizations to scale their data stack as their data needs grow.

Production considerations

When it comes to taking the data stack to production, there are a lot of other considerations. Organizations should ensure reliable, fault-tolerant backups, set up security and role-based access, and build DBT models to cater to multiple use cases and normalize data values across sources. Other considerations may include monitoring and alerting, performance tuning, and disaster recovery planning.

Reliable, fault-tolerant backups are crucial to ensure that data is not lost in the event of a disaster. Organizations should have a well-defined backup and recovery plan in place. This should include regular backups, offsite storage of backups, and testing of backups to ensure they can be restored in case of an emergency.

Security and role-based access are also crucial considerations. Organizations should ensure that only authorized personnel have access to sensitive data. This can be achieved by setting up role-based access controls, which ensure that only users with the necessary permissions can access sensitive data.

Building the DBT models to cater to multiple use cases, normalizing data values across data sources, etc., are also essential. Organizations should ensure that their data is accurate, consistent, and reliable. This can be achieved by building DBT models that cater to multiple use cases and normalizing data values across data sources.

Finally, monitoring and alerting, performance tuning, and disaster recovery planning are also important. Organizations should ensure that their data stack is performing at optimal levels and that they are alerted to any issues that arise. Performance tuning is also necessary to ensure that the data stack is performing at optimal levels. Disaster recovery planning is also crucial to ensure that data can be recovered in the event of a disaster.

Conclusion

In conclusion, data stacks have become increasingly affordable and accessible for organizations of all sizes. The open-source data stack ecosystem has grown significantly, providing powerful alternatives for every layer of the stack. Organizations should seriously consider adopting a data stack framework over a patchwork of point-to-point integrations to drive growth and operations. A data stack framework provides a more structured approach to data management, making it easier to manage, and providing the organization with the ability to gain insights from their data

Deploying a data lake to production with all these elements is a non-trivial technical exercise. If you do not have this expertise in-house you should consider using the services of a consulting organization with expertise in this area like Incentius. Drop us an email at info@incentius.com and we’d be happy to help.

 

Modern Database Management Best Practices Amit Jain May 20, 2022

As the volume of company data has increased, database management has become increasingly critical. Rapid data expansion causes a slew of undesirable outcomes, including poor application performance and compliance risk, to mention a few. 

Database management entails a variety of proactive strategies for mitigating the negative consequences of data accumulation. Database Management is the process of organizing, storing, and retrieving data from a computer. It may also refer to a Database Administrator’s (DBA) data storage, operations, and security procedures throughout the data’s life cycle. Organizations may prevent events that impair productivity and income and increase data integration for more business intelligence by controlling data across its full lifespan.

1. Draft Relevant Business Goals:

A strong data management vision, clear targets, well-defined metrics to assess progress, and a solid business purpose are all part of a well-developed data strategy. There are a plethora of other things you can do with your data, but it’s critical to start by defining your objectives. If you know what your business goals are, you can keep just the data that is useful to the organization, making database maintenance a breeze. It’s critical for your DBAs to understand the plan for the data they’re collecting and to concentrate entirely on the data that’s important to the company’s overall objectives. 

Your company needs should be reflected in an executable, focused database management plan, as well as the metrics you’ll use to measure your performance. Knowing your company’s business objectives allows you to maintain only the data that is relevant to your company, making database maintenance and administration easier. Setting meaningful company objectives is the very first practice you should consider because it offers you a guiding light so you don’t get lost.

2.Clear Policies and Procedures should be crafted:

When implementing best database management practices, you must establish rules and processes for your database settings. Creating particular backup and recovery processes and regulations allows your team to respond more quickly in the event of a disaster. Standard methods for deleting old files, conducting maintenance, and indexing files should be included in policies. These standards limit the risk of misunderstandings or errors, which is especially crucial in bigger businesses with various datasets and database managers.

Data should also be verified for correctness on a regular basis, as obsolete data might be useless to your organization. Most significantly, having explicit policies makes database maintenance and day-to-day management much easier. Lastly but not least, rules should contain procedures for erasing data and securely destroying storage media such as hard disks and servers.

3. Ensure High Standard of Data Quality:

Data is the King you do not want to mess with. Or as they say today- Data is the new Oil. Your DBA should try to maintain a high level of data quality by eliminating data that does not match the criteria and adjusting quality standards to reflect your evolving strategy. Even if they don’t work directly with the DBA or the database, everyone in your firm should understand the principles of data quality protection. Someone who is unaware of the dangers of duplicating data might add to your team’s workload. 

Teach everyone how to submit high-quality data and how to recognise good data. Train all team members who have access to the data on the right procedures to gather and enter data to help your team focus on data quality. You must set clear goals for improving data quality using relevant and quantifiable metrics. Make sure the stewards are included in the process when data managers develop objectives. These database management practices pay a lot in the short as-well-as long run.

4.Data Security and Backup must be a priority:

When it comes to database management in your company, data security and backup are key responsibilities. There are never enough backups when it comes to data. It’s critical to have a reliable backup system in place and to keep an eye on it to make sure it’s working properly. Furthermore, in the event of data loss or corruption, every organization should have a disaster recovery strategy in place. Because disasters can happen, it’s critical for your company to have a data recovery policy in place. 

Although no disaster can be completely predicted or avoided, you can strengthen your database’s data security and manage the risks associated with worst-case situations. If or when a possible breach occurs, having a strategic process in place is critical. Security concerns evolve in tandem with technological advancements, corporate expansion, and database features. Your staff should keep current with the market and seek to anticipate the demands of your database.

Here’s your Takeaway-Use Quality Data Management Services!

Choosing appropriate services is a crucial step in establishing a high-quality data management system for your business. Keep in mind that the ultimate objective is to have a modular architecture that can connect to and structure a number of data sources and analysis methods from the start. 

This is when Incentius comes into picture. You want to locate a customer data platform that will provide you with a truthful and concise view into your connections and user data, as well as the ability to communicate with your market precisely and promptly. You want your data management system to make your tasks simpler by automatically enriching and cleaning data to guarantee you have the most comprehensive and full picture of your data. We are here, consider bothering us for it.