White Paper: Inspur Group Co. Ltd
Apache Spark is a fast and general engine for large-scale data processing. To handle increasing data rates and demanding user expectations, big data processing platforms like Apache Spark have emerged and quickly gained popularity.
This whitepaper on “Optimizing Apache Spark with Memory1”demonstrates that by leveraging Memory1 to maximize the available memory, the servers can do more work (75% efficiency improvement in Spark performance), unleashing the full potential of real-time, big data processing.
How Spark works?
What are the issues that subvert the full potential of Apache Spark’s disaggregated approach?
How to simulate the critical demands of a typical Spark operations workload?
How to eliminate the hardware cost concerns traditionally faced in multi-server Spark deployments?
What efficiency metrics are involved in the Spark operations?
Key issues faced by Spark in traditional, DRAM-only deployments
How to Avoid the high cost of DRAM-only implementations in Apache Spark architects
Four undeniable trends shape the way we think about data – big or small. While managing big data is ripe with challenges, it continues to represent more than $15 trillion in untapped value. New analytics and next-generation platforms hold the key to unlocking that value. Understanding these four trends brings light to the current shift taking place from Big Data 1.0 to 2.0, a cataclysmal shift for those who fail to see the shift and take action, a catalyst for those who embrace the new opportunity. Key take aways from this white paper: 5 Advancements of Big Data 1.0 5 Challenges of Big Data 1.0 Big Data 2.0 – Transformational Value Big Data 2.0 Means Big Value Times Ten
By: Intersec Group
The advent of open‐source technologies fueled big data initiatives with the intent to materialize new business models. The goal of big data projects often revolves around solving problems in addition to helping drive ROI and value across a business unit or entire organization. It’s often difficult to launch a big data project quickly due to competing business priorities; the myriad of technology choices available as well as, the sheer size, volume, and velocity of data. Key questions from this whitepaper: What are the common questions and challenges that the operators are facing when starting a Big Data project? What are the best practices to avoid being trapped in the ever‐lasting big data project that fails to generate any revenue? Should the big data project be carried out by the IT department or should it be led by a dedicated organization, under a new function like a Chief Data Officer, distinct from traditional IT?
What is Big Data ?
Big data is the act of collecting huge amount of enterprise data that can be used for future analysis. Big data helps all kind of industry data to grow securely that includes government, banking, retail, education and healthcare. Big data stores all kind of data including structured, unstructured and semi structured data, which contains valuable information about core functions of an enterprise such as finance, marketing, procurement.
What is Technology ?
Technology is the use of scientific knowledge for creating tools, processing actions and extracting of materials whether in industry or in our everyday lives. We apply technology in nearly all things that we do in our lives, we use technology at work, in communication, transportation, making food, extracting and securing information, running an organization and many more tasks, pretty much everywhere. Types of technology include information technology, banking technology, medical technology,
What is Data Warehouse ?
A data warehouse (DW) is a central repository or central database of numerous corporate information and data derived from operational systems and external data sources. In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for data analysis. It is considered as a core component of business intelligence.