Business strategies and operations have changed drastically since March 2020 as a result of the unfortunate pandemic. The recessionary environment altered industry outlooks, and the ever-changing forecasts of sales and operations, have made all business models unpredictable.
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
Want to develop a Big Data Processing Application?
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The annual Analytics India Salary report presented by AIM and AnalytixLabs is the only annual study in India that delves into salary trends and provides a comprehensive view of the changing landscape of analytics salaries. The report, now in its seventh year, look at the distribution of average salaries across several categories including years of experience, metropolitan regions, industries, education levels, gender, tools, and skills.
The Data Analytics function is experiencing significant growth and development in terms of skills, capabilities, and funding. Last year, despite the pandemic, the Indian start-up industry witnessed $836.3 million investment, almost a 10% (9.7%) increase than the previous year. Also, more than one in five (21%) analytics teams across firms in India witnessed a growth in the last 12 months and the post-pandemic job market saw an upswing of data science jobs. The development of the data science domain is evidenced by the high salaries drawn by analytics professionals across the organization, with Analytics professionals doing relatively well in spite of the pandemic.
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