How is Orchestration Fixing Big Data Management Challenges For Digital Enterprises?
If global companies manage their data efficiently, they would never have to lock horns with deploying data science strategies.
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If global companies manage their data efficiently, they would never have to lock horns with deploying data science strategies. It does sound simple until you know that ‘efficiently’ is an ocean of complexities and beyond the expertise of most businesses. Capturing and orchestrating data is a rough path but a necessity to undo data silos and provide immediate results to on-demand search queries. And that is why we need data orchestration that is purposefully meant to enable quick access and yet protect it from widespread fragmentation.
How Does Big Data Orchestration Work?
Orchestration tools are a middleware layer between the data warehouse (source points) and the business applications such as CRM, BI Analytics, etc. Through pre-built connectors and API adapters, these tools perform rapid integration of all source points with the main silos. It simplifies data monitoring from a single point while enabling the teams to perform seamless fetching. All of this is achieved without having to develop custom scripts or devoting additional resources.
Why Is Orchestration Important?
In a traditional system landscape with no orchestration, data analysis depends upon hitting multiple sources to fetch the smallest of data sets.
Obviously, the process is slow and inefficient in generating the required results. Moreover, it increases dependency upon other resources who have deep knowledge about the systems that others don’t.
As per the study, 80% of data science effort goes into capturing and preparing the data for analysis. Yet it is prone to insufficiencies. With orchestration, enterprises can significantly lessen the time & resources exhausted. It automates the capturing and aligning tasks while preparing the data sets for access.
To further complicate the practice, the technology transforms every few years while causing a major overhaul on the data management experience. This means that a decade-old company may not be relevant in the current business setup if it hasn’t embraced new-gen practices including orchestration.
Enforcing Data Governance is Another Practice Largely Dependent Upon Orchestration
Now, data governance is important to sustain trust in the implemented security strategies. However, implementing governance protocols is difficult when the data stream is originating from multiple sources. Orchestration connects all data channels thereby making it easier to implement a governance strategy without leaving any loopholes. Since incoming data is tracked in real-time, make sure your orchestration strategy works in compliance here. Ideally, the orchestration tools should block any data packets at the source that do not pass through the tracking protocol.
Catch Up With Increasing Data Scale and Complexity
In the wake of propelling the data landscape over the past few years, enterprises are locking horns with capturing and putting data into productive analytics. Today, a contemporary infrastructure to organize such a humungous pool of data demands a dedicated resource house. Moreover, new-age technologies such as the Internet-of-Things (IoT), Artificial Intelligence, or the Blockchain are further speeding up the production of data in real-time. If left unattended, personal information pertaining to millions of users will be lost causing crisis-like circumstances. As per a 2019 study by DOMO, Americans alone consume more than 40 lakhs GB of internet content every single minute.
In fact, the enterprise readiness to tackle this volume is lagging behind new-age technologies, sources, and platforms generating these data sets. With data orchestration, digital enterprises have an opportunity to implement strategic protocols and align the mammoth.
That being said, lack of orchestration is not the pain point. In all honesty, data orchestration emerged as a regular practice a decade ago when the ‘big data revolution was catching up. Today, the caliber of available orchestration tools is under scrutiny. While all of them do collect & organize, the question is to what extent. Despite numerous attempts to utilize practices such as micro-DB, companies continue to fear slow data, duplicate data, or insufficient data.
The K2View Difference
Among many others, K2View’s Data Fabric procures a Micro-DB approach to handle the rush with finesse. With a focus on smart orchestration and uninterrupted processing, their platform ensures a seamless protocol designing of data flows in a succession of phases called Stages.
So it’s like breaking down data sets into millions of micro-sized databases and hitting the requested micro-DB only. Their platform has pre-defined data operators that act as the building blocks of data flows and further assemble to execute processing.
Here, the data orchestration mechanism ensures:
- Data flow management front end produced from different Stages executed left-right. This flow is constituted of different execution paths and can be accessed on-demand based on configurable logical paths.
- A list of more than one hundred pre-defined operators for processing Micro-DB data in real-time. Custom build operators can also be added to the library.
- A Debug mode to visualize data tracing across multiple orchestration flows.
Unlike previous attempts, Data Fabric successfully converts scattered data sets into singular digital entities each representing a specific business object. To put it simply, 95% of the data access occurs within a specific micro DB because it was organized based on business needs in the first place.
The data management practices of the future will demand lightning-fast processing regardless of the volume. Likewise, the management ecosystem should provide risk-free integration, ease of configuration, embedded data services, and a performance-oriented processing engine. Therefore, enterprises must plan and prepare for a decade-long stint to stay competitive in these agile business times.
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