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One reality that many companies face when adopting cloud technology: designing data infrastructure for business in a cloud computing environment is just different. Legacy stacks can indeed suffice for many companies. But as business requirements grow and use cases increase, both in number and complexity, the models and assumptions that worked well enough in the data center become problematic.
If you are a Data Leader in 2022, Data Governance is most definitely on your radar. Regardless of your organization's data maturity stage, chances are, you have already implemented or started implementing a Data Governance Strategy.
The modern data stack or the Data Stack is a collection of cloud-native applications that serve as the foundation for an enterprise data infrastructure.
“There must be something wrong with Excel. I can't get these numbers to make sense.” For anyone who has had a similar experience of staring at a spreadsheet for far too long, we have news for you: Excel isn’t the problem; your data is.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
Without a clear and quick process your dev, sales, and customer success teams can become overwhelmed by the amount of work required to delight new customers and ingest clean validated data.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
The data ecosystem has changed drastically over the last six years and we've witnessed the rise and fall of several different technologies. However, there’s one constant that’s remained the same, the cloud data warehouse.
Change data capture (CDC) is the process of recognising when data has been changed in a source system so a downstream process or system can action that change. A common use case is to reflect the change in a different target system so that the data in the systems stay in sync.
The current data engineering ecosystem is filled with a wide range of tools from both open-source and third-party solutions. While there still isn’t a consensus on which path to choose (to either choose an open-source or third-party vendor), I think it’s interesting to explore the possibilities of building an open-source data stack ( and, with the current state of the market, it’s honestly the best time to re-consider how you designed your data stack and begin to explore open-source alternatives).
As data became more and more available to companies, data integration became one of the most crucial challenges for organizations. In the past decades, ETL (extract, transform, load) and later ELT (extract, load, transform) emerged as data integration methods that transfer data from a source to a data warehouse.
In our experience at Secoda working with many data teams, we've seen most data teams do not have the tools they need to succeed. For growing organizations, the data function is usually an afterthought. The first data hire is brought on before raising a Series A and is expected to manage the workload that comes afterward with little to no support.
From personal experience, I have always found it interesting to learn how to create an organized catalog of data. However, this interest was transformed into a passion when I began to realize the amount of time and effort it could save me within my job responsibilities. Creating a data catalog can greatly help you with organizing the data they collect, therefore making it easier to find what you need when you need it.
Building a data practice is not only about making technological choices; and you will likely have to start with a first iteration and expect it to evolve as your business grows.
Let’s not mince words. Product led growth (PLG) isn’t something that happens overnight. It has to infuse company culture and involves commitment from every team - not just the go-to-market teams on the front lines.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
The modern data stack is on the rise. Many companies use raw data from their SaaS analytics tools as input for their data warehouse, but this introduces problems downstream. Are there better ways?
Breaking down some of the problems I’ve seen in data collaboration and offering advice on how to make better, faster decisions with collaborative analytics.
The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
