Data Factory is a fully managed, cloud-based, data-integration ETL service that automates the movement and transformation of data. A. The generic two-level data warehouse architecture includes which of the following? When the action is triggered after the result, new RDD is not formed like transformation. Following is a concise description of the nine-step KDD process, Beginning with a managerial step: 1. It develops the scene for understanding what should be done with the various decisions like transformation, algorithms, representation, etc. It’s an open standard; anyone may use it. 1. Option B shows a strong positive relationship. Both editions include the same features; however, Cloud Native Edition places limits on: The number of records in your data set on which you can run automated discovery or data transformation jobs; The number of jobs that you can run each day to transform data or assign terms; The number of accepted assets in the enterprise data catalog CHAPTER 9 — BUSINESS INTELLIGENCE AND BIG DATA MULTIPLE CHOICE 1. ... DTS is an example of a data transformation engine. Quiz #1 Question 1 1 out of 1 points Which of the following statements about Big Data is true? Data transformation includes which of the following? Data transformation is the process of converting data or information from one format to another, usually from the format of a source system into the required format of a new destination system. The reciprocal transformation, some power transformations such as the Yeo–Johnson transformation, and certain other transformations such as applying the inverse hyperbolic sine, can be meaningfully applied to data that include both positive and negative values (the power transformation is invertible over all real numbers if λ is an odd integer). Regarding data, there are many things to go wrong – be it the construction, arrangement, formatting, spellings, duplication, extra spaces, and so on. Using a mathematical rule to change the scale on either the x- or y-axis in order to linearise a non-linear scatterplot. Two types of Apache Spark RDD operations are- Transformations and Actions.A Transformation is a function that produces new RDD from the existing RDDs but when we want to work with the actual dataset, at that point Action is performed. 3 Data Selection - Next step is Data Selection in which data relevant to the analysis task are retrieved from the database. a. Five key trends emerged from Forrester's recent Digital Transformation Summit, held May 9-10 in Chicago. Solution: (A) The data is obtained on consecutive days and thus the most effective type of analysis will be time series analysis. Spark RDD Operations. The following table lists sample messages for log entries for a very simple package. Pure Big Data systems do not involve fault tolerance. The most prolific is UTF-8, which is a variable-length encoding and uses 8-bit code units, designed for backwards compatibility with ASCII encoding. Unicode Transformation Format: The Unicode Transformation Format (UTF) is a character encoding format which is able to encode all of the possible character code points in Unicode. It also includes about the activities of function oriented design, data-flow design along with data-flow diagrams and the symbols used in data-flow diagrams. Data transformation activities should be properly implemented to produce clean, condensed, new, complete and standardized data, respectively. As mentioned before, the whole purpose of data preprocessing is to encode the data in order to bring it to such a state that the machine now understands it. 20) What type of analysis could be most effective for predicting temperature on the following type of data. Because log (0) is undefined—as is the log of any negative number—, when using a log transformation, a constant should be added to all values to make them all positive before transformation. A. Second step is Data Integration in which multiple data sources are combined. 7. To perform the data analytics properly we need various data cleaning techniques so that our data is ready for analysis. Data that can extracted from numerous internal and external sources ... A process to upgrade the quality of data before it is moved into a data warehouse Ans: B 20. A negative value for RMSE b. Following transformation can be applied Data transformation: Data transformation operations would contribute toward the success of the mining process. Through the data transformation process, a number of steps must be taken in order for the data to be converted, made readable between different applications, and modified into the desired file format. Common transformations of this data include square root, cube root, and log. 1. For left-skewed data—tail is on the left, negative skew—, common transformations include square root (constant – x), cube root (constant – x), and log (constant – x). 5.1 Introduction. In data mining pre-processes and especially in metadata and data warehouse, we use data transformation in order to convert data from a source data format into destination data. For example, the cost of living will vary from state to state, so what would be a high salary in one region could be barely enough to scrape by in another. Cube root transformation: The cube root transformation involves converting x to x^(1/3). A) Time Series Analysis B) Classification C) Clustering D) None of the above. Artificial intelligence c. Prescriptive analytics d. . The theoretical foundations of data mining includes the following concepts − Data Reduction − The basic idea of this theory is to reduce the data representation which trades accuracy for speed in response to the need to obtain quick approximate answers to queries on very large databases. (a) KDD process (b) ETL process (c) KTL process (d) MDX process (e) None of the above. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is the dominant data-mining process framework. Like a factory that runs equipment to transform raw materials into finished goods, Azure Data Factory orchestrates existing services that collect raw data and transform it into ready-to-use information. Areas that are covered by Data transformation include: cleansing - it is by definition transformation process in which data that violates business rules is changed to conform these rules. Data Architecture Issues. The data architecture includes the data itself and its quality as well as the various models that represent the data, ... We’ll address each area in the following sections. The lowest possible value for RMSE c. The highest possible value for RMSE d. An RMSE value of exactly (or as close as possible to 1) and the process steps for the transformation process from data flow diagram to structure chart. Smoothing: It helps to remove noise from the data. (a) Business requirements level MapReduce is a storage filing system. Selected Answer: Pure Big Data systems do not involve fault tolerance. At least one data mart B. Data transformation operations change the data to make it useful in data mining. The following list describes the various phases of the process. Data transformations types. Data for mapping from operational environment to data warehouse − It includes the source databases and their contents, data extraction, data partition cleaning, transformation rules, data refresh and purging rules. Visualisation is an important tool for insight generation, but it is rare that you get the data in exactly the right form you need. b) Contains numerous naming conventions and formats. c) Organized around important subject areas. This is the initial preliminary step. Often you’ll need to create some new variables or summaries, or maybe you just want to rename the variables or reorder the observations in order to make the data a little easier to work with. What is ETL? _____ includes a wide range of applications, practices, and technologies for the extraction, transformation, integration, analysis, interpretation, and presentation of data to support improved decision making. Sqaured transformation- The squared transformation stretches out the upper end of the scale on an axis. Building up an understanding of the application domain. Feature encoding is basically performing transformations on the data such that it can be easily accepted as input for machine learning algorithms while still retaining its original meaning. C. a process to upgrade the quality of data after it is moved into a data warehouse. Hadoop is a type of processor used to process Big Data applications. a) Can be updated by end users. ETL, for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.. ETL was introduced in the 1970s as a process for integrating and loading data into mainframes or supercomputers for computation and analysis. Sample Messages From a Data Flow Task. d) Contains only current data. D. a process to upgrade the quality of data before it is moved into a data warehouse. At which level we can create dimensional models? Business understanding: Get a clear understanding of the problem you’re out to solve, how it impacts your organization, and your goals for addressing […] Which of the following indicates the best transformation of the data has taken place? Data preparation is the process of gathering, combining, structuring and organizing data so it can be analyzed as part of data visualization , analytics and machine learning applications. Reasons a data transformation might need to occur include making it compatible with other data, moving it to another system, comparing it with other data or aggregating information in the data. A strong positive correlation would occur when the following condition is met. A. a process to reject data from the data warehouse and to create the necessary indexes. A data warehouse is which of the following? a. The slope of the line would be positive in this case and the data points will show a clear linear relationship. The package uses an OLE DB source to extract data from a table, a Sort transformation to sort the data, and an OLE DB destination to writes the data to a different table. Which of the following process includes data cleaning, data integration, data selection, data transformation, data mining, pattern evolution and knowledge presentation? Data forms the backbone of any data analytics you do. Lineage of data means the history of data migrated and transformation applied on it. Answers: Data chunks are stored in different locations on one computer. For example, databases might need to be combined following a corporate acquisition, transferred to a cloud data warehouse or merged for analysis. B. a process to load the data in the data warehouse and to create the necessary indexes. 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