Technology is always evolving to adapt for upcoming challenges and provide efficient solutions to the modern day problems. Below is the list of 8 tech trends that will dominate the year in the field of Data.
Multi-Cloud and Hybrid Cloud Environments
It is estimated that public cloud services investment will see health growth this year. Companies worldwide move towards hybrid, multi-cloud, and edge environments. This new cloud hosting paradigm enabled innovative distributed data processing architectures.
Businesses moving from private infrastructure to public, hybrid, and multi-cloud solutions will experience a significant increase in agility. Behind this phenomenon lies a connection to a rise in the production of unstructured data.
Hybrid cloud approaches will see an increase in adoption in 2022. By adopting a hybrid cloud, a Business can keep its sensitive data private while combining it with more cost-effective public cloud solutions. This is especially true for many SMEs. Hybrid clouds for data processing combine cost and security in a balanced and agile way by taking away the cost to run a private infrastructure.
MLOP
MLOP, or the management of the lifecycle of machine learning (ML) projects, will become increasingly important when we develop long-term solutions and not just proofs-of-concept (PoCs).
Machine Learning Operations involves a set of processes or rather a sequence of steps implemented to deploy an ML model to the production environment. There are several steps to be undertaken before an ML Model is production-ready. These processes ensure that your model can be scaled for a large user base and perform accurately.
MLOps means developing systematic approach to the monitoring, scalability and evaluation of data pipelines and ML models. Model training should be reproducible and deployment should be as automated as possible.
The ability to rapidly build end-to-end solutions allows for an improved focus on providing genuine business value.
Augmented Analytics
Augmented Analytics (or simply AA) refers to data analytics tools and processes that rely on Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) techniques. Traditionally handled by a data engineer or a data scientist, AA systems will deliver real-time automated insights.
The results of AA promise to be more accurate, leading to better decisions. With this approach, Data experts can now focus on exploring and generating in-depth reports and predictions.
Augmented analytics will more than likely experience massive growth in 2022. Augmented analytics also enables non-technical people to rip the benefits of data analytics technology.
AA helps technical and non-technical users understand data through visual AI-assisted analytics. In particular, this technology is a perfect match for online retail platforms that usually generate massive amounts of data that needs processing and analysis.
For instance, take a look at this Power BI Dashboard (fully interactive)
Data in Motion
Real-time data beats slow data. That’s true for almost every business scenario; no matter if you work in retail, banking, insurance, automotive, manufacturing, or any other industry.
If you want to fight against fraud, sell your inventory, detect cyber attacks, or keep machines running 24/7, then acting proactively while the data is hot is crucial.
Event streaming powered by Apache Kafka became the de facto standard for integrating and processing data in motion. Building automated actions with native SQL queries enables any development and data engineering team to use the streaming data to add business value.
The Move From Open Source to SaaS
Dhruba Borthakur, Co-Founder and CTO, Rockset
While many individuals love open-source software for its ideals and communal culture, companies have always been clear-eyed about why they chose open-source: cost and convenience.
Today, SaaS and cloud-native services trump open-source software on all of these factors. SaaS vendors handle all infrastructure, updates, maintenance, security, and more. This low ops serverless model sidesteps the high human cost of managing software, while enabling engineering teams to easily build high-performing and scalable data-driven applications that satisfy their external and internal customers.
2022 will be an exciting year for data analytics. Not all of the changes will be immediately obvious. Many of the changes are subtle, albeit pervasive cultural shifts. But the outcomes will be transformative, and the business value generated will be huge.
Cloud-Native Platforms
Cloud-native platforms in the data ecosystem.These platforms, which promise scalability and adaptability, are a response to both performance and cost control The transition to the cloud has multiple advantages, including cost and time savings, reliability, and mobility
Cloud-native platforms, which exploit the basic capabilities of cloud computing to provide scalable and elastic IT capabilities “as a service”, are expected to form the basis of 95% of companies’ digital transformation projects by 2025, compared with 40% in 2021
The work from home or the hybrid model of working has encouraged various companies to make a shift and transfer their data to the cloud
The millennial’s and other employers are drawn to the organizations using the latest tools and technologies, which further will help to advance the business
ML-Powered Coding Assistants: GitHub Copilot
GitHub Copilot, announced last year, is now prime time-ready. Copilot is an AI-powered service that helps developers write new code by analyzing already existing code as well as comments. It helps with the overall developers’ productivity by generating basic functions instead of us writing those functions from scratch. Copilot is the first among many solutions to come out in the future, to help with AI-based pair programming and automate most of the steps in the software development lifecycle.
Nikita Povarov, in the article AI for Software Developers: a Future or a New Reality, wrote about the role of AI developer tools. AI developers may attempt to use algorithms to augment programmers’ work and make them more productive; in the software development context, we’re clearly seeing AI both performing human tasks and augmenting programmers’ work.
Data Mesh
Much in the same way that software engineering teams transitioned from monolithic applications to microservice architectures, the data mesh is, in many ways, the data platform version of microservices.A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Borrowing Eric Evans’ theory of domain-driven design, a flexible, scalable software development paradigm that matches the structure and language of your code with its corresponding business domain.
Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, domain-specific data consumers and views “data-as-a-product,” with each domain handling their own data pipelines. The tissue connecting these domains and their associated data assets is a universal interoperability layer that applies the same syntax and data standards.
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