According to Gartner research, by 2025, artificial intelligence requirements will have drastically increased the volume of data for a whopping 75% of enterprises. Big data orchestration is how organizations can prepare.
Addressing Big Data’s Big Challenges
Data is quickly becoming one of the most critical assets available to the enterprise –critical for day-to-day operations, product development, marketing, and more. Data fuels the analytics that optimize processes, shipping routes, inventory, and business decisions, while improving the AI and machine learning algorithms that underlie a host of new technologies, from chat bots to digital twins.
The need for data-driven business models will continue to increase as consumer expectations, market pressures, and technologies evolve. But first there are 3 central challenges that organizations must address before effectively scaling and leveraging big data initiatives.
The older data is, the less useful it becomes. This is especially true in logistics and operations, where small changes can quickly impact entire systems. In order to make informed decisions, decision makers need data that is highly relevant and therefore as up-to-date and complete as possible. As a result, the need for real-time or near real-time data analytics is increasing, putting strains on infrastructure, data pipelines, and budgets.
Data is diverse. It can be structured, unstructured, or semistructured, or it can be batched or streaming data. Data can be stored in Hadoop clusters, or through Amazon EMR or, increasingly, closer to the end-point as edge computing technologies mature. In order to be useful, big data must be extracted, processed, and made readily available to users and applications across the enterprise (hopefully in real-time).
Organizations must be able to standardize and centrally manage governance across disparate data localities, while navigating a patchwork of regulatory regimes based on geography, while managing evolving security requirements.
The issue at the heart of these challenges is that much of an organization’s data resides in silos. Data platforms, BI tools, and data analytics software have been deployed on an ad-hoc basis, while big data/Hadoop initiatives are, for the most part, undertaken by individual departments.
Complicating this issue is the oftentimes slow roll-out of cloud compute. Databases and applications are migrated to public or private clouds independently, leaving key data stores on-premises (often to simplify compliance requirements). Managing big data between disparate on-prem data centers is one thing –big data management in a hybrid-cloud environment has its own challenges.
Further complicating these challenges is that organizations are increasingly reliant on multiple cloud vendors in order to avoid vendor lock-in. Silos no longer exist on-premises, but across cloud providers, too.
What is Big Data Orchestration?
Big data orchestration refers to the centralized control of processes that manage data across disparate systems, data centers, or data lakes. Big data orchestration tools enable IT teams to design and automate end-to-end processes that incorporate data, files, and dependencies from across the organization, without having to write custom scripts.
Big data orchestration tools are middleware that sit above data warehousing tools such as Hive and Spark, and below the platforms and applications (AI, BI, CRM, etc.) that utilize the data.
By providing prebuilt connectors and low-code API adapters, data orchestration platforms enable IT to rapidly integrate new data sources and existing data silos into ETL and big data processes. Big data orchestrators also make it possible for IT to manage data access, provision resources, and monitor systems from a centralized location. This can all be done without having to develop new custom scripts, and can be done across on-premises data warehouses and cloud-based databases.
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Data Orchestration Layer in the Cloud
More reliance on cloud-based infrastructure is almost inevitable as IT and enterprises seek value from increasingly large big data stores. Building and maintaining physical data centers is cost prohibitive for many organizations, while even adding servers into existing infrastructure is slow and inefficient.
In order to support a growth in big data, IT teams are taking to the cloud (Amazon AWS, Google Cloud, Microsoft Azure, etc). This is generally done for two reasons:
- Cost Reduction.
Cloud-computing is often cheaper than installing, configuring, and maintaining on-premises servers, which run the risk of sitting idle. Power and cooling don’t have to be accounted for, updates and patches are handled by the vendor, and, with the right tools, day-to-day functions can be easily automated.
A common problem with managing data on-premises is that data volumes are rarely static. There are, at times, far larger workloads than usual. Cloud-based infrastructure enables IT to quickly scale resources to meet unpredictable spikes in demand, provisioning and deprovisioning virtual machines based on dynamic, real-time needs.
However, most legacy, on-premises platforms were designed for homogeneous IT stacks, which poses a problem –how to efficiently manage data between incompatible storage systems. The answer in part is to create a data orchestration layer between on-prem and cloud-based data stores. The orchestration layer manages the integration of disparate tools, making it easier for IT teams to automate and manage data across hybrid-cloud and multi-cloud environments.
Big Data Orchestration Tools
Big data orchestration tools provide the integration capabilities needed for IT teams to develop and manage data processes that span disparate technologies. These tools can be listed under a variety of names –service orchestration, workload automation, hybrid data integration, etc.– but for the most part include a standard set of capabilities.
- API adapters – IT teams can easily integrate virtually any existing (or future) technology, across hybrid and multi-cloud environments
- Script-language independent – IT can still write scripts, if needed, in any language they choose
- Resource provisioning – AI and ML algorithms intelligently manage infrastructure based on dynamic, real-time needs and historical data analyses
- Monitoring – real-time process monitoring enables auto-remediation, optimization, and alerting
Future of Big Data
As new technologies evolve, big data is likely to become more heterogeneous and complex. Today, advancements in IoT, 5G, and edge computing are moving data closer to the “edge” –for example, compute and processes being run at endpoints in hospitals, manufacturing, fulfillment centers, and more. This requires new connections to IT infrastructure.
Meanwhile, a growth in applicable use cases will mean that increasing volumes of big data will be necessary to train high-performance AI tools and more.
At the same time, governments and privacy concerns will further develop into patchworks of data regulations that will need to be adopted and adapted to on a somewhat continuous basis as regulations evolve.
This will all have to be juggled, navigated, and orchestrated. In order to meet these demands, IT teams will need to centralize control over file systems and data processes by leveraging extensible automation tools to orchestrate and streamline the delivery of data across the organization.
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