Data Preparation Using Tools Alteryx and Looker
The most prominent and dreadful task of any data scientist is Data Processing. The quality of the data analysis relies solely on how efficiently the data is preprocessed. Data Processing is an elaborate process in which raw data is cleaned and transformed before processing and analyzing the data. Hence it makes it the most prominent prerequisite for all data related studies. The process usually includes standardizing data formats, enriching source data, adding or removing outliers (data points). An efficient data preparation should result in top-quality data, error-free data and data that helps make better business decisions. The entire process includes multiple steps like gathering the data, discovering and accessing the data, cleansing and validating, transforming and enriching and finally storing the data. Though the entire process appears to be cumbersome, on a brighter side, it can be automated using data preparation tools.
Let us explore tools like Alteryx and Looker.
Looker is an enterprise platform for business intelligence, data applications and analytics. An ideal tool to visualize, explore and share the data and achieve better analytical solutions for the company. Looker as a tool comes with a lot of features like finding, organizing, sending and sharing the content, retrieve and chart data, creating dashboards and reports.
Looker uses its own language called LookML which is used to describe dimensions, calculations and data relationships in a SQL database. Looker uses a model that is written in LookML to construct SQL queries against a database. LookML separates content of queries from structure of queries. Query structure includes how the tables are joined and query content includes which columns to access, derived fields, aggregating functions to compute and what filtering expressions to apply. LookML comes with predefined data types and syntax for data modeling, which is very easy. LookML is a case-sensitive language. Since the Looker IDE is also case-sensitive, any error in the syntax is indicated and thus editing the query is made easier. A LookML project is a collection of Models, Views and LookML Dashboards. Model files contains information on which file to use and how to join two files. View files helps in calculating the dimensions, measures and field sets of each file or multiple files, if they are joined. LookML Dashboards represents the visualizations.
A Look in Looker is a snapshot of the queried data. Looker Dashboard shows a collection of tiles that shows visualized query results. Content can be looked up either in the form of Look or a Dashboard.
Data sharing from Looker can also be very easy. One can create a query and email the results to anyone. The Look or Dashboard can also be shared via email to anyone or even to other data destinations. Look can be published as a public URL even to users who do not use Looker. Data or visualizations can be scheduled to be sent to users automatically too. Looker allows us to create an alert to notify the users for any change in the dashboard tiles or when it exceeds a specific threshold. Looker can also be integrated with third-party services via an action hub server.
Looker lets you connect with multiple data sources, allows various deployment methods, meanwhile ensuring transparency, security and privacy. It allows database connections with Redshift, BigQuery, Snowflake, PostgreSQL, SAP HANA, Microsoft SQL Server and many others.
With all the above-mentioned features, Looker uses powerful data modeling that abstracts the data complexity at any scale and create a common data model for the entire organization. Looker and Google Cloud Platform together provides the fastest analysis and find insights in the datasets. Looker makes one of the easiest data exploration platforms with which data is accessed and managed in the most intuitive way that the organization demands.
Another platform used by most of the data analysts is Alteryx. An end-to-end platform used to automate analytics and machine learning processes. The prominent features of the platform include data preparation, data blending, diagnostic reporting, geospatial analytics, prescriptive analytics, predictive auto ML. Data can be inputted from various data platforms and the optimized data output is given in the form of dashboards or documents. Innumerable data sources (Amazon, Oracle, Salesforce) can be connected to Alteryx and the platform takes the least time in producing the optimized results.
The platform is designed in such an efficient way that it requires no prior coding or analytics expertise. Complex R-based models are built on unstructured data and this also does not demand coding skills. These models can be built once and can be automated forever, thus drastically reducing the time taken for data preparation. The automated outcomes can be shared as visualizations or dashboards.
While Looker is a data-discovery platform that helps in getting real-time access to data to make efficient business decisions, Alteryx is a data analytics platform that blends and prepares data from various sources into corporate workflows. The costing of Looker is slightly higher than Alteryx and hence it is not apt for startups. The choice of the analytics platform can be decided based on the organization’s needs.
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