Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. This comes with the advantages of reduction of redundancy, data integrity and consequently, less storage usage.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

Knowledge Hut

MARCH 27, 2024

Essential in programming for tasks like sorting, searching, and organizing data within algorithms. Examples MySQL, PostgreSQL, MongoDB Arrays, Linked Lists, Trees, Hash Tables Scaling Challenges Scales well for handling large datasets and complex queries. Flexibility: Offers scalability to manage extensive datasets efficiently.

Database 52

Database Relational Database Algorithm Data Storage 52

Monte Carlo

MAY 28, 2024

Ensuring all relevant data inputs are accounted for is crucial for a comprehensive ingestion process. Data Extraction : Begin extraction using methods such as API calls or SQL queries. Batch processing gathers large datasets at scheduled intervals, ideal for operations like end-of-day reports.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

AltexSoft

JUNE 26, 2023

We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection? It’s the first and essential stage of data-related activities and projects, including business intelligence , machine learning , and big data analytics. No wonder only 0.5

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

MARCH 19, 2015

Relational Databases – The fundamental concept behind databases, namely MySQL, Oracle Express Edition, and MS-SQL that uses SQL, is that they are all Relational Database Management Systems that make use of relations (generally referred to as tables) for storing data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

Knowledge Hut

DECEMBER 26, 2023

If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

Knowledge Hut

DECEMBER 21, 2023

Hadoop helps in data mining, predictive analytics, and ML applications. Why are Hadoop Big Data Tools Needed? With the help of Hadoop big data tools, organizations can make decisions that will be based on the analysis of multiple datasets and variables, and not just small samples or anecdotal incidents.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Edureka

AUGUST 2, 2023

Python offers a strong ecosystem for data scientists to carry out activities like data cleansing, exploration, visualization, and modeling thanks to modules like NumPy, Pandas, and Matplotlib. SQL Structured Query Language, or SQL, is used to manage and work with relational databases.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Knowledge Hut

AUGUST 16, 2024

Mathematics / Stastistical Skills While it is possible to become a Data Scientist without a degree, it is necessary to have Mathematical skills to become a Data Scientist. Let us look at some of the areas in Mathematics that are the prerequisites to becoming a Data Scientist.

Data Science 52

Data Science Hadoop Unstructured Data Programming Language 52

Knowledge Hut

JANUARY 30, 2024

In summary, data extraction is a fundamental step in data-driven decision-making and analytics, enabling the exploration and utilization of valuable insights within an organization's data ecosystem. What is the purpose of extracting data? The process of discovering patterns, trends, and insights within large datasets.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

ProjectPro

JANUARY 25, 2022

Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. Because of its interoperability, it is the best framework for processing large datasets. Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage.

Big Data 52

Big Data Data Process Process Kafka 52

Monte Carlo

FEBRUARY 27, 2024

Right now, RAG is the essential technique to make GenAI models useful by giving an LLM access to an integrated, dynamic dataset while responding to prompts. But instead of integrating a dynamic database to an existing LLM, fine-tuning involves training an LLM on a smaller, task-specific, and labeled dataset.

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52

phData: Data Engineering

SEPTEMBER 19, 2023

With the amounts of data involved, this can be crucial to utilizing a data lake effectively. Metadata Management can be performed manually by creating spreadsheets and documents notating information about the various datasets. It follows a predefined schema and enforces data normalization and standardization.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. AWS Lake Formation architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

JUNE 23, 2016

Sqoop is compatible with all JDBC compatible databases. Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Apache Sqoop uses Hadoop MapReduce to get data from relational databases and stores it on HDFS. It has a connector based architecture.

Hadoop 40

Hadoop MySQL Relational Database Java 40

ProjectPro

NOVEMBER 15, 2021

Multi-node, multi-GPU deployments are also supported by RAPIDS, allowing for substantially faster processing and training on much bigger datasets. TDengine Source: www.taosdata.com TDengine is an open-source big data platform tailored for IoT , linked automobiles, and industrial IoT. Trino Source: trino.io

Big Data 52

Big Data Project Metadata Programming Language 52

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Knowledge Hut

DECEMBER 29, 2023

Data Mining Data science field of study, data mining is the practice of applying certain approaches to data in order to get useful information from it, which may then be used by a company to make informed choices. It separates the hidden links and patterns in the data. Data mining's usefulness varies per sector.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Rockset

JANUARY 5, 2022

Hopefully we can understand how SQL databases aren’t necessarily bound by the limitations of yesteryear, allowing them to remain very relevant in an era of real-time analytics. A Brief History of SQL Databases SQL was originally developed in 1974 by IBM researchers for use with its pioneering relational database, the System R.

Database 52

Database SQL NoSQL Raw Data 52

ProjectPro

JULY 13, 2015

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop.

Hadoop 52

Hadoop Big Data Data Analytics Banking 52

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

ProjectPro

OCTOBER 15, 2014

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. It is Hive that has enabled Facebook to deal with 10’s of Terabytes of Data on a daily basis with ease. Hive is similar to a SQL Interface in Hadoop.

Hadoop 52

Hadoop Java Unstructured Data SQL 52

Rockset

AUGUST 2, 2023

This enrichment data has changing schemas and new data providers are constantly being added to enhance the insights, making it challenging for Windward to support using relational databases with strict schemas. The performance of Snowflake was evaluated on a Large virtual data warehouse that is $16/hr in AWS US-West.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

AltexSoft

NOVEMBER 10, 2021

Prior to the recent advances in data management technologies, there were two main types of data stores companies could make use of, namely data warehouses and data lakes. Data warehouse. Traditional data warehouse platform architecture. Unstructured and streaming data support. websites, etc.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Edureka

FEBRUARY 8, 2024

Use cases for memory-optimized instances include- Database Servers- Applications like relational databases benefit from the higher memory capacity to store and retrieve data efficiently. In-Memory Caching- Memory-optimized instances are suitable for in-memory caching solutions, enhancing the speed of data access.

AWS 52

AWS NoSQL Deep Learning Machine Learning 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. Step 1- Automating the Lakehouse's data intake.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

NOVEMBER 14, 2015

Data Integration 3.Scalability Specialized Data Analytics 7.Streaming This failure of relational database management systems triggered organizations to move their data from RDBMS to Hadoop. Data migration from legacy systems to the cloud is a major use case in organizations that have been into relational databases.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Knowledge Hut

OCTOBER 30, 2023

Data Transformation and ETL: Handle more complex data transformation and ETL (Extract, Transform, Load) processes, including handling data from multiple sources and dealing with complex data structures. Ensure compliance with data protection regulations. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

ProjectPro

AUGUST 24, 2016

Table of Contents Need for HBase HBase –Understanding the Basics HBase Architecture Explained Components of Apache HBase Architecture HMaster Region Server Zookeeper Need for HBase Apache Hadoop has gained popularity in the big data space for storing, managing and processing big data as it can handle high volume of multi-structured data.

Architecture 40

Architecture IT Hadoop NoSQL 40

AltexSoft

MARCH 14, 2023

Databases store key information that powers a company’s product, such as user data and product data. The ones that keep only relational data in a tabular format are called SQL or relational database management systems (RDBMSs). Data storage component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

JULY 27, 2021

Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

OCTOBER 8, 2021

The tool supports all sorts of data loading and processing: real-time, batch, streaming (using Spark), etc. ODI has a wide array of connections to integrate with relational database management systems ( RDBMS) , cloud data warehouses, Hadoop, Spark , CRMs, B2B systems, while also supporting flat files, JSON, and XML formats.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

U-Next

AUGUST 8, 2022

This is a must-know language since it is the industry standard for communicating with relational databases. Data science specialists must be able to query databases, and a good grasp of SQL is essential for any aspiring Data Scientist. calculating the maximum and lowest values in a given data collection.

Data Science 40

Data Science Coding Programming Language Scala 40