DATA ENGINEERING

Take the help of our team for effective data conversion, cleaning, entry, scrapping and scanning services.

Data Warehousing

Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations. We deploy decision support technologies that help us utilize the data available in a data warehouse accurately and efficiently. These technologies help client side to use the warehouse quickly and effectively. We build the system in a way, they can gather data, analyze it, and take decisions based on the information present in the warehouse.

Data Scraping

Data scraping is the process of importing information from a website into a spreadsheet or local file saved on your computer. It’s one of the most efficient ways to get data from the web, and in some cases to channel that data to another website. And that list’s just scratching the surface. Data scraping has a vast number of applications – it’s useful in just about any case where data needs to be moved from one place to another. Metaorigin implements Web scraping in a variety of digital businesses that rely on data harvesting. Legitimate use cases include: Search engine bots crawling a site, analyzing its content and then ranking it. Price comparison sites deploying bots to auto-fetch prices and product descriptions for allied seller websites. Market research companies using scrapers to pull data from forums and social media (e.g., for sentiment analysis).

Data Lake

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Metaorigin Data Lake includes all the capabilities required to make it easy for developers, data scientists and analysts to store data of any size, shape and speed, and do all types of processing and analytics across platforms and languages. It removes the complexities of ingesting and storing all of your data while making it faster to get up and running with batch, streaming and interactive analytics. Metaorigin works with existing IT investments for identity, management and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so you can extend current data applications.Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags.

Data Visuallsation

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.  In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be.

Data Analytics

Infused with AI, Metaorigin Business Analytics help small and large organizations maximize the value of their data, unearth insights, build plans and respond in real-time to customer demand. Metaorigin data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Big Data Analytics

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Metaorigin provides Big Data analytics —it can be used for better decision making, preventing fraudulent activities, among other things. based on likes, shares, search history, and more. What enables this is the techniques, tools, and frameworks that are a result of Big Data analytics.

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