Learn more about data fabric addressing the challenges of modern data management.
On average, data-driven companies grow more than 30% every year. Because of the competitive advantage that data confers to incumbents who are capable of extracting value from it, it has been called the new oil.
Companies are tapping into this well of resources because of the advantages that it has to offer:
- Organizations can build data products using artificial intelligence and machine learning. These vastly outperform humans at menial tasks, accelerate production, and automate processes.
- Companies can scale without massive disruptions to their organizational structures when accommodating increased supply and demand.
- Digital transformation allows companies to permeate the benefits of data throughout their operations in order to further enrich and spread the advantages that it has to offer.
But using data to run your operations poses its own set of challenges:
- Data engineers and data scientists who work with data spend around 80% of their time on data cleaning, management, and just general maintenance.
- Companies suffer from huge data integrity and reliability issues. The overhead of managing data causes issues with data quality. In fact, only 3% of companies’ data meets basic quality standards.
What are the challenges of modern data management?
The average organization nowadays looks drastically different from one that was around 10 years ago. The revolution in data diversity, quality and quantity has brought a completely different aspect to data management.
With the onset of Software as Service, many companies have moved from using one software to run their business to using several. Companies are now advertising APIs to programmatically set digital marketing, using a multitude of CRMs to manage contacts, making use of ticketing systems to keep track of support issues, and reaping the benefits of software for financials and billing. It’s safe to say that companies are tapping into the SaaS growth to speed up their own development.
In addition to this performance data, organizations also need to have the infrastructure necessary to run the business: companies have multiple on-premise databases and use what the cloud infrastructure has to offer to digitalize their processes.
The typical data that a company produces and consumes is thus distributed across a myriad of resources, all of which need to be maintained, integrated, and synced to get the most out of them.
This data ecosystem gives rise to specific data management challenges:
- Data Access. With multiple distributed systems, it is hard to keep control over who should have access to specific resources and at which security level. At the same time, accessible data is needed if analysts and data consumers are to maximize the potential of data for business growth.
- Data Security. Data is stored in multiple locations, and access over a myriad of endpoints. Keeping all of the different apps, APIs, and endpoints secure causes a lot of overhead for the IT department.
- Data governance and regulatory compliance. Working with a distributed data system makes it harder to maintain the regulatory standards for data compliance, as well as implementing the principles of modern data governance. Whether it be issues with tracing data flowing from one system into another, requests for data deletion and anonymization (GDPR), or dealing with the increased system complexity of multiple data sources and integrations - companies are struggling to fulfill their management and compliance practices. As if this weren’t enough, each technology comes with a different set of tools and technological standards, so you’re probably heading towards management hell.
- Limited fixed choices. Implementing an infrastructure often results in choices from which the company cannot detach. Once the entire data ecosystem is deployed on a specific set of software, it’s hard to integrate new data sources (if the existing technology does not support integrations) or transform existing data (if the transform layer is technically hard to implement). Technological lock-in can seriously dampen the ability of a company to work fast and act on data.
- … (and many more).
How does data fabric help to solve modern data management challenges?
Gartner identified data fabric as the response to the challenges of modern data management:
“Data fabric enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage.”
Data fabric is the common term used to describe the architecture and set of data services within a platform, which unify the distributed ecosystem of data assets for the end user.
Unlike traditional approaches, data fabric keeps the data distributed instead of trying to centralize it in storage.
What is the difference between data fabric and ETL or ELT?
Traditional data pipelines revolved around the ETL or ELT architecture. That is, you would extract data (transform it) and load it into a relational database, data lake, or data warehouse. This is a great process for preparing business data and structuring it in such a way that it produces valuable business insights.
However, there are certain advantages to data fabric architecture that go beyond the traditional approaches:
- Source-to-source enrichments. Data can flow from one source to another, without first being stored in a central data store. This avoids the need to store all the data into a central repository first, and hence allows data to be available to all actors when they need it.
- Multi-platform access and ingestion. From on-premise data stores in the cloud and even hybrid cloud environments, the data fabric architecture offers high data availability without the need to centralize infrastructure.
But data fabric is not just beneficial in comparison to traditional data pipelines; it also offers advantages of its own.
What are the advantages of data fabric?
- Single architecture. By relying on a single architecture that weaves data into a single data fabric, organizations aren’t faced with issues of data not flowing between different (organizational, infrastructural, or other) data silos. Single architectures also lessen the management burden of juggling multiple non-interacting distributed systems.
- Scale. Data fabric scales with your company’s needs.
- Flexibility. Widen the capabilities of what your system has to offer by having a flexible infrastructure. Add different relational databases or new inflowing data sources by simply linking it with the existing data fabric architecture. Don’t be limited to the vendors that you’ve picked in the past.
- Security. A single architecture means that it’s easier to manage the security aspects of your data ecosystem. From conferring access rights to limiting usage rights, working with a singular platform removes the security overhead.
- Reliability. From backups to replications, data fabric implements the logic for reliable workflows.
- Automation. Automate the common tasks in data fabric to save time and free up more hours for revenue-generating work.
Keboola: the data fabric of the future, available today
Keboola - the all-in-one DataOps platform - was built to make the work of data practitioners easier.
When designing your data fabric architecture, Keboola can help you to speed up the process at a fraction of the cost:
- With its plug-and-play design, pick the technological stack that you want to test or implement and deploy it in a matter of clicks.
- Centralize your entire data architecture within Keboola itself. No need to change platforms or write extended documentation - Keboola centralizes the entire know-how within the platform. This offers a single environment for frictionless data collection, integration, storage, and access.
- Experiment with new stacks without adding to your overhead. It’s simply a matter of picking a different connector within the GUI. Vendor lock-in and technological debt are now things of the past.
- Scale with ease. Keboola natively scales to different speeds and volumes of data without breaking down and causing you infrastructural hiccups.
- Automate your processes. Automation is one of the leading principles behind Keboola, eliminating the need to manually adjust and set configurations to make it work every time.
- Collaborate. Whether you work with data engineers, data scientists, or data ops, Keboola centralizes the data pipeline and tooling for all actors to work within the same data environment.
- Keep your data secure - both at rest and in transit - with Keboola’s top-in-class data security.
- Future proof your entire data management process. It doesn’t matter if you change your mind tomorrow, Keboola’s flexibility allows you to always add new resources, transformations, and infrastructure on top (and interwoven with) the existing one without any snags along the way.
Curious about what Keboola can do for you? Try Keboola yourself with this free tier.
Yes, you read this correctly - Keboola now has a completely free tier! From today until always.