In recent months, the term “data fabric” has joined the lexicon of data management and analytics buzzwords. In fact, Gartner recently identified “data fabric” as one of the “Top 10 Data and Analytics Technology Trends for 2019.” As with any hot new tech term, you might be wondering: “What is data fabric?” and “Why do I need it?”
In simplest terms, a data fabric is a single environment consisting of a unified architecture, and services or technologies running on that architecture, that helps organizations manage their data. The ultimate goal of data fabric is to maximize the value of your data and accelerate digital transformation.
What is Data Fabric? now.
The purpose of data fabric
Think of data fabric as a weave that is stretched over a large space that connects multiple locations, types, and sources of data, with methods for accessing that data. The data can be processed, managed, and stored as it moves within the data fabric. The data can also be accessed by or shared with internal and external applications for a wide variety of analytical and operation use cases for all organizations – including advanced analytics for forecasting, product development, and sales and marketing optimization. The goals are many: to improve customer engagement through more advanced mobile apps and interactions, comply with data regulations, and optimize supply chains, to name a few.
Of course, the devil is in the details. What exactly constitutes a data fabric differs based on someone’s role (analyst vs executive vs data engineer vs data scientist vs line of business data analyst). But the premise that a data fabric enables accessing, ingesting, integrating, and sharing of data in a distributed data environment is widely accepted. More specifically, a data fabric:
- Connects to any data source via pre-packaged connectors and components, eliminating the need for coding
- Provides data ingestion and integration capabilities – between and among data sources as well as applications
- Supports batch, real-time, and big data use cases
- Manages multiple environments – on-premises cloud, hybrid, and multi-cloud – both as data source and as data consumer
- Provides built-in data quality, data preparation, and data governance capabilities, bolstered by machine learning augmented automation
- Supports data sharing with internal and external stakeholders via API support
Data drives competitive advantage for every business
We are living in a time of unprecedented pace of change – change across the pace of business and innovation. In this paradigm, data drives competitive advantage for every business to succeed and thrive, and organizations need to deliver data quickly to serve business and customer needs. In fact, according to a recent Forrester study, insight-driven businesses are growing at an average of more than 30% annually.
Recognizing this, more organizations are trying to obtain additional value from their data in a variety of ways, including creating new revenue streams and reducing costs through operational efficiencies. However, with the prevalence of the cloud and Internet of Things, along with increasingly cheaper storage and processing, data is no longer bound to on premises data centers. There is more data, more types of data, and it is in many more locations, making it much more difficult to manage.
Challenges of managing your data
Succeeding in this environment and becoming a data-driven organization is not easy. There are many roadblocks on the way to becoming a digital leader. As organizations use more and more applications, their data becomes increasingly siloed and inaccessible beyond its initial scope. While legacy infrastructures and systems only exasperate the problem, data can become siloed when trying to migrate to the cloud. It can be especially difficult to share data between data residing on different public clouds (e.g. AWS and Azure) or between a public cloud and on-premise data center, or storing it all in a cloud data warehouse.
A typical company today has data in multiple on-premise locations as well as multiple public and/or private clouds. The data is both structured and unstructured and maintained in a wide variety of formats – file systems, relational databases, SaaS applications, etc. And, processing that data spans a multitude of technologies, from batch ETL or ELT processing to changed data capture to real-time streaming. With almost three quarters of organizations (74%) using 6 or more data integration tools, it becomes very difficult for organizations to be nimble and quickly ingest, integrate, analyze, and share their data and incorporate new data sources.
As the amount and the sources of data continue to increase, the problem only gets worse. As a result, data professionals end up spending 75% of their time on tasks other than data analysis. Not only does this considerably inhibit the ability of organizations to get the most out of their data in a timely manner, it is also a grossly wasteful and unproductive use of your data professionals’ time.
In addition to the roadblocks preventing organizations from having rapid access to data, there are also a myriad of issues that make it difficult for the data itself to be trustworthy. In fact, almost half of enterprise data has integrity issues. And, it is 10 times more costly to get any work done that relies on data if the underlying data has flaws.
The Definitive Guide to Data Integration now.
Data fabric to the rescue
Implementing a data fabric to manage the collection, governance, integration, and sharing of data can help organizations meet these challenges and become a digital leader. A data fabric is not a one-off fix to a specific data integration or management problem. It is a permanent and scalable solution to manage all of your data under a unified environment.
Ultimately, implementing a data fabric can help an organization meet their data management challenges and become digital leaders by:
- Providing a single environment for accessing and collecting all data, no matter where it’s located and no matter how it’s stored – eliminating data silos
- Enabling simpler and unified data management, including data integration, quality, governance, and sharing, by eliminating multiple tools and providing faster access to more trustworthy data
- Delivering greater scalability that can adapt to increasing data volumes, data sources and application
- Making it easier to leverage the cloud by supporting on-premise, hybrid and multi-cloud environments and faster migration between these environments
- Reducing reliance on legacy infrastructures and solutions
- Future proofing their the data management infrastructure as new data sources and end points, along with new technologies, can be added onto the data fabric without disrupting existing connections or deployments
How to get trusted data at speed
Talend Data Fabric offers the breadth of capabilities needed by modern data-driven organizations in a unified environment with a native architecture that enables them to adapt to changes faster with embedded data integrity. Talend’s unique differentiators make it possible to deliver to both trust and speed without forced compromise.
The Definitive Guide to Cloud Data Warehouses and Cloud Data Lakes now.
Talend provides a unified environment for all your needs to help you transform raw data to trusted data. Talend Data Fabric eliminates the need for multiple data integration products, contracts, and support mechanisms. From discovery and ingestion, to integrating data from multiple sources, to cleansing that data and ensuring its integrity, to ultimately analyzing and sharing the data with stakeholders.
Native code generation
Talend generates optimized code natively – in Java/Spark/SQL – in building data pipelines to take advantage of all leading platforms (such as AWS, Azure, or Snowflake). This, along with Talend’s 1,000+ built in connectors and components for leading applications and environments, makes it easy to work with code and when building pipelines.
On-premise or cloud
In addition, Talend Data Fabric is also natively designed to work on both on-premise and cloud environments. Run Talend to ingest and integrate data from both on premises back office environments, such as Oracle and SAP, and cloud environments such as AWS, Azure, Google Cloud, or Snowflake. Quickly embrace new cloud-based technologies, such as containers with Docker and Kubernetes, advanced analytics with Databricks, Qubole, Spark, and serverless computing
Pervasive data quality and governance
Talend Data Fabric has integrated data quality into each step of data management – whether you are discovering and ingesting data, using Talend for data stewardship and setting out roles for data cleansing, or need to trace data lineage to ensure compliance and integrity. Talend Data Fabric is designed for IT and the business to collaborate and share data with self-service data management.
Now that you know more about what a data fabric is and how it works, we invite you to download a free trial of Talend Data Fabric and see what your data can really do.