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.

 

Introduction to Data Engineering and its Importance Sumeet Shah May 26, 2022

What is Data Engineering?

Data is the new oil’. Heard of it? Well, it’s tough to describe data engineering accurately. Data Engineering is a field that deals with data analysis and tasks such as obtaining and storing data from various sources. It entails planning and building the data infrastructure required to gather, clean, and format data so that it is accessible and usable to end users. This procedure guarantees that data is both valuable and accessible. Data engineering is primarily concerned with the practical applications of data collecting and processing. Data Engineering improves the efficiency of data science. Although it appears to be an easy job, it necessitates a high level of data literacy.

Importance of Data Engineering

Technological advancements have had a significant influence on the vitality of data throughout time. We used to create traditional data warehouses, give BI reporting, and perform upgrades and maintenance on such platforms. In today’s world of innovative startups and businesses, we’re building with new tools for the modern world. Cloud computing, open-source initiatives, and the massive expansion of data are all examples of these breakthroughs.

Data is there at every stage of the route, whether business teams are dealing with sales data or evaluating their lead life cycles. We no longer construct data warehouses. We’re creating data lakes and real-time data streams instead. In the era of big data, more controlled data implies more accurate forecasts. Data engineering is critical because it enables companies to optimize data for usability. There is no data without data engineering. There is no machine learning or AI without data. Data science requires data to run algorithms on.

If you don’t want to fall behind, focus on data engineering today so you can move on to deep analytics and data science before it becomes too late.

Role of Data Engineers

The “engineering” element is the key to knowing what data engineering is. For operational usage, data engineers construct up analytics databases and data pipelines. Much of their work entails preparing large amounts of data and ensuring that data flows are as smooth as possible. These pipelines must collect data from a variety of sources and store it in a single warehouse that represents it consistently as a single source of truth. The objective of data engineers is to ensure that data is not just abundant but also clear. Part of the work entails formatting both structured and unstructured data. Data that is structured can be stored in a database. They require essential abilities such as programming, mathematics, and computer science, as well as experience and soft skills in order to convey data patterns that aid corporate success. Data engineers are in charge of overseeing an organization’s analytics. Your data is given mobility by data engineers.

Data Engineers also handle some of the on-and-off duties, such as:

  1. Interaction with management to have a better understanding of the company’s goals
  2. New data validation procedures and data analysis tools are being developed
  3. Ensuring that data governance and security standards are followed
  4. Identifying ways for increasing data accuracy and efficiency
  5. Discovering tasks that will be automated using the required data

Conclusion

“Without a systematic way to start and keep data clean, bad data will happen.” — Donato Diorio

 It should go without saying that data is useless unless it can be read. As a result, data engineering is the initial stage in turning data into meaningful information. Nearly every goal of the business necessitates the use of data engineering. To prepare and analyze data for future study, data engineers employ a variety of skills and technologies. Analysts and scientists will be unable to access and operate with data without the infrastructure established by data engineers. And as a result, organizations risk losing access to one of their most precious assets. For company scalability, Incentius recognises the necessity of data engineering. That’s why we offer quality Data Engineering services to take your company to the next level.

The role of Data lakes Marketing October 4, 2021

The role of data lakes is very significant. The ability to gather more information from even more sources in little time, as well as enabling people to interact and analyze data in new ways, translates to improved quality improvement. A data lake contains all data sets, unorganized and semi-structured, from a large range of data sources, making it far more versatile in terms of its potential applications.

What is a data lake? 

A data lake can be defined as a large collection of unprocessed data. It is a big storage area that can keep a large amount of data (mainly unorganized) in its raw form for future evaluation. Data lakes take advantage of data warehouses’ biggest flaw: their lack of flexibility.

Why should startups choose data lakes? 

If you are new to the world of business and have just started an online business, you might be wondering about how and where to store the data. Data lakes can help you store all kinds of data, organized or unorganized. Data lakes provide a comprehensive and broader perspective of data. They use a more adaptable approach to data analysis and emphasize the significance of all data. There are various reasons why your startup needs to choose data lakes: 

Scalability at a lower cost: 

A very significant advantage of a Data Lake to any company, especially a startup is the capacity to store an enormous amount of information for a low cost, which is less than that of a defined data company warehouse.

Ensures data availability:

Data lake ensures that all employees have access to the data stored, no matter what their designation is. They have the option to use a specific part of the data that serves their purpose during a meeting or presentation. 

Preserves raw data:

Since all the data, organized or not, can be stored in data lakes, it makes for a great space that allows a person to preserve raw data and explore it later when there is a requirement. It can be used in the future for data exploration and data analytics. 

Provide room for creativity:

If you are someone with an entrepreneurial mindset, then you should always use a tool that does not restrict your creativity. For a startup who is still learning the dos and don’ts of the world, building habits that restrict their imagination might not be the best idea. Data lakes don’t bind businesses to certain analytics and insight models. 

How do data lakes benefit startups? 

Democratizing data, obtaining better quality data, providing scalability at a lower cost, being versatile and adaptable for all kinds of format, supporting multiple other languages other than SQL, and using advanced analytics to support business growth and make the process more efficient are some of the ways that data lakes can help a startup grow.

Which is more cost-efficient: Data lake or data warehouse? 

Building data lakes is substantially less expensive than building data warehouses. A warehouse can take quite a long time to construct from the ground up, making it highly costly in the end. Using advanced analytics to store data is less expensive than using a data warehouse. This is because data technologies are frequently open-source, which means that license and community support are both free. The data technologies are intended for use with low-cost commodity hardware. A data warehouse can be expensive to store, especially if the amount of data is large. A data lake, on the other side, is made for cost-effective storage.

Conclusion:

There are multiple benefits of using a data lake. It makes for a practical and important tool for data storage for startups.