Towards Data Science

JULY 9, 2024

While Parquet based data lake storage, offered by different cloud providers, gave us the immense flexibilities during the initial days of data lake implementations, the evolution of business and technology requirements in current days are posing challenges around those implementations.

Data Lake 73

Data Lake Datasets Data Storage Database 73

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

ProjectPro

FEBRUARY 8, 2023

This serverless data integration service can automatically and quickly discover structured or unstructured enterprise data when stored in data lakes in Amazon S3, data warehouses in Amazon Redshift, and other databases that are a component of the Amazon Relational Database Service.

AWS 98

AWS Scala Metadata Data Lake 98

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

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

JANUARY 19, 2022

Here are some role-specific skills you should consider to become an Azure data engineer- Most data storage and processing systems use programming languages. Data engineers must thoroughly understand programming languages such as Python, Java, or Scala. Who should take the certification exam?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

Knowledge Hut

APRIL 23, 2024

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Data warehousing offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Knowledge Hut

NOVEMBER 17, 2023

Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

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

Knowledge Hut

MARCH 12, 2024

In this article, I will examine the principal distinctions and similarities between SQL vs SQLite databases. Relational databases can be interacted with using this computer language. Data kept in relational databases is managed using the programming language SQL. What is SQL? What is SQLite?

SQL 52

SQL Relational Database PostgreSQL MySQL 52

phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

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

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source. Metadata layer The metadata layer manages and organizes the metadata associated with the data that’s been ingested and stored.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source. Metadata layer The metadata layer manages and organizes the metadata associated with the data that’s been ingested and stored.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Cloud Academy

JANUARY 27, 2022

For example, it’s good to be familiar with the different data types in the field, including: variables varchar int char prime numbers int numbers Also, named pairs and their storage in SQL structures are important concepts. These fundamentals will give you a solid foundation in data and datasets.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

JULY 26, 2023

It also has strong querying capabilities, including a large number of operators and indexes that allow for quick data retrieval and analysis. Database Software- Other NoSQL: NoSQL databases cover a variety of database software that differs from typical relational databases. Columnar Database (e.g.-

Database 52

Database NoSQL MongoDB Telecommunication 52

Knowledge Hut

JULY 24, 2023

SQL is a powerful tool for managing and manipulating relational databases, and it continues to be widely used in the industry today. Companies of all sizes and across various sectors utilize SQL for data analysis and reporting as the volume of data generated daily increases.

Database 52

Database SQL Process NoSQL 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

Knowledge Hut

JUNE 23, 2023

Data warehousing to aggregate unstructured data collected from multiple sources. Data architecture to tackle datasets and the relationship between processes and applications. Coding helps you link your database and work with all programming languages.

Data Engineering 52

Data Engineering Data Engineer Engineering NoSQL 52

Knowledge Hut

JUNE 30, 2023

Developed by the famous tech giant Microsoft, SQL Server is a durable DBMS that offers a vast range of features for the management of relational databases. They are used to organize data into different tables, which consist of rows and columns, and follow a relational model. Microsoft SQL Server: What is DBMS in SQL?

MySQL 52

MySQL Medical Relational Database Database 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

Knowledge Hut

SEPTEMBER 26, 2023

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. This is particularly valuable in today's data landscape, where information comes in various shapes and sizes.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

AltexSoft

MAY 12, 2023

Structured data is formatted in tables, rows, and columns, following a well-defined, fixed schema with specific data types, relationships, and rules. A fixed schema means the structure and organization of the data are predetermined and consistent. You can’t just keep it in SQL databases, unlike structured data.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

JANUARY 19, 2022

Source: Image uploaded by Tawfik Borgi on (researchgate.net) So, what is the first step towards leveraging data? The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

MARCH 20, 2024

They are responsible for establishing and managing data pipelines that make it easier to gather, process, and store large volumes of structured and unstructured data. Data engineers must know about big data technologies like Hive, Spark, and Hadoop. Large datasets can be analyzed effectively due to these technologies.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 52

ProjectPro

NOVEMBER 5, 2014

Hadoop is the way to go for organizations that do not want to add load to their primary storage system and want to write distributed jobs that perform well. MongoDB NoSQL database is used in the big data stack for storing and retrieving one item at a time from large datasets whereas Hadoop is used for processing these large data sets.

MongoDB 40

MongoDB Hadoop NoSQL Big Data 40

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Data Integration 3.Scalability

Hadoop 52

Hadoop Project Big Data Healthcare 52

Edureka

JULY 16, 2024

It removes the issues related to the machine learning pipeline and provides an integrated setup for comprehensive model creation. SageMaker, on the other hand, works well with other AWS services and provides a sound foundation to deal with large datasets and computations effectively.

AWS 52

AWS Algorithm Machine Learning Amazon Web Services 52

Knowledge Hut

NOVEMBER 28, 2023

ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location. Data Visualization To successfully fulfill ETL or ELT-related work, you must be well-versed in exploratory data analysis (EDA).

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

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

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. Another type of data storage — a data lake — tried to address these and other issues. Data lake.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

ProjectPro

JULY 27, 2021

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

SEPTEMBER 21, 2023

From those home-made beginnings as Compass, Elasticsearch has matured into one of the leading enterprise search engines, standing among the top 10 most popular database management systems globally according to the Stack Overflow 2023 Developer Survey. Each document is a collection of fields, the basic data units to be searched.

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

NOVEMBER 22, 2021

What's the difference between an RDD, a DataFrame, and a DataSet? RDDs contain all datasets and dataframes. If a similar arrangement of data needs to be calculated again, RDDs can be efficiently reserved. It's useful when you need to do low-level transformations, operations, and control on a dataset. Output- Q13.

Hadoop 52

Hadoop Python Datasets Metadata 52