Being able to skillfully and efficiently manipulate big data is a useful skill to have for data analysts, data scientists and anyone working with data. In this post, we will look at side-by-side comparisons of pandas code snippets for basic data manipulation tasks and their counterparts in PySpark.
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.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
We live in the era of big data. With the growth of the internet, data is rapidly growing in a huge amount and with high variability. Big data processing could give you a headache because it naturally takes a lot of running time. Apache Spark (or Spark) is one of the popular tools to process big data.
Spark is a unified analytics engine for large-scale data processing. With Spark, we can perform data processing quickly and distribute processing tasks across multiple computers. People use Spark because it is deployable in popular programming languages such as Python, Scala, Java, R and SQL. It also has a stack of libraries that support streaming data, machine learning and graph processing.
One of the Spark interfaces is PySpark that allows you to write Spark applications using Python APIs. PySpark supports Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. As someone working a lot with Pandas, I found that PySpark can do what I do there. However, the implementation is quite different. I will list some of the data processing tasks I usually perform in Pandas and translate them to PySpark.
#pandas #big-data-analytics #data-engineering #pyspark #spark #pandas data processing tasks translated to pyspark
Data cleaning and manipulation are essential steps in any data science project. Both **SQL **and **Pandas **are popular tools used by Data Analysts and Data Scientists nowadays.
Which tool to used depends on where the data is stored, what kind of data format, and how we want to use it.
In following article, I am going to compare SQL and Pandas when implementing basis data manipulations. Hope it to be useful to someone who is familiar with SQL and would like to learn about Pandas, and vice versa.
#sql #pandas #data-manipulation #python #data-science #data manipulation: sql vs. pandas
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data