answer:To find Z's share in the profit, we first need to calculate the ratio of their investments taking into account the time for which they invested. X invested Rs. 36000 for 12 months (since X was there from the beginning till the end). Y invested Rs. 42000 for 12 months (since Y was there from the beginning till the end). Z invested Rs. 48000 but only for 8 months (since Z joined after 4 months). Now, let's calculate their investment ratios: X's investment ratio = 36000 * 12 Y's investment ratio = 42000 * 12 Z's investment ratio = 48000 * 8 Now, let's calculate the actual numbers: X's investment ratio = 36000 * 12 = 432000 Y's investment ratio = 42000 * 12 = 504000 Z's investment ratio = 48000 * 8 = 384000 The total investment ratio is the sum of X's, Y's, and Z's investment ratios: Total investment ratio = X's investment ratio + Y's investment ratio + Z's investment ratio Total investment ratio = 432000 + 504000 + 384000 Total investment ratio = 1320000 Now, we find the share of each partner in the profit based on their investment ratios: X's share = (X's investment ratio / Total investment ratio) * Total profit Y's share = (Y's investment ratio / Total investment ratio) * Total profit Z's share = (Z's investment ratio / Total investment ratio) * Total profit Since we want to find Z's share, we will calculate that: Z's share = (384000 / 1320000) * 14190 Now, let's calculate the actual number: Z's share = (384000 / 1320000) * 14190 Z's share = (0.2909090909) * 14190 Z's share = 4127.27272727 Rounding to the nearest rupee, Z's share in the profit is approximately Rs. boxed{4127} .

answer:Sophia is checking in after a long time and inquiring about the new job in Portland. She suggests presenting the community garden project at an upcoming conference on urban agriculture and disaster resilience in Seattle in two months, seeing it as a chance to reconnect and share the work.

answer:ETL stands for Extract, Transform, and Load. The ETL process is a series of steps used to extract data from multiple sources, transform it into a standardized format, and then load the transformed data into a target system, such as a data warehouse, database, or data lake. The purpose of the ETL process is to integrate data from various sources, such as transactional databases, applications, and external data sources, into a single, unified view. This enables organizations to analyze and report on the data more effectively, gain insights, and make informed business decisions. During the ETL process, data is first extracted from the source systems, which may involve querying databases, reading files, or accessing external APIs. The extracted data is then transformed into a standardized format, which may involve data cleansing, data mapping, and data aggregation. Finally, the transformed data is loaded into the target system, where it can be accessed and analyzed by business users, analysts, and data scientists. The ETL process is typically used in data integration, data warehousing, and business intelligence applications, as it enables organizations to create a single, unified view of their data and gain insights into their business operations.

answer:Data transformation involves converting data from one format to another to make it more suitable for analysis, reporting, or other purposes. There are several types of data transformation, including: Data aggregation, which involves combining multiple data values into a single value, such as calculating the sum or average of a set of numbers. Data aggregation is often used to reduce the complexity of large datasets and provide a higher-level view of the data. Data cleansing, which involves identifying and correcting errors or inconsistencies in the data, such as missing values, duplicates, or invalid data types. Data cleansing is essential to ensure that the data is accurate and reliable. Data mapping, which involves transforming data from one format to another, such as converting data types or reformatting data fields. Data mapping is often used when integrating data from different sources or systems. Data normalization, which involves scaling numeric data to a common range, such as between 0 and 1, to prevent differences in scales from affecting analysis. Data pivoting, which involves rotating or transforming data from rows to columns or vice versa. Data pivoting is often used to transform data from a normalized format to a denormalized format, making it easier to analyze. Data summarization, which involves creating summary statistics, such as means, medians, or counts, to provide a concise overview of the data. Data type conversion, which involves converting data from one data type to another, such as converting text to numbers or dates to strings. Data subsetting, which involves selecting a subset of data from a larger dataset, such as selecting a specific date range or geographic region. These are just a few examples of data transformation types, and the specific type of transformation used depends on the nature of the data and the goals of the analysis.