Analytics Vidhya

JUNE 30, 2022

Introduction Hive is a popular data warehouse built on top of Hadoop that is used by companies like Walmart, Tiktok, and AT&T. This article was published as a part of the Data Science Blogathon. It is an important technology for data engineers to learn and master. It uses a declarative language called HQL, also known […].

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Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Smart Data Collective

OCTOBER 14, 2019

With big data careers in high demand, the required skillsets will include: Apache Hadoop. Software businesses are using Hadoop clusters on a more regular basis now. Apache Hadoop develops open-source software and lets developers process large amounts of data across different computers by using simple models. NoSQL and SQL.

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Big Data Big Data Apache Hadoop Hadoop 75

Alation

FEBRUARY 20, 2020

We decided to address these needs for SQL engines over Hadoop in Alation 4.0. It is also used across Alation’s applications, such as our SQL query writing interface, Compose, which produces SmartSuggestions. Further, Alation Compose now benefits from the usage context derived from the query catalogs over Hadoop.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. This is particularly advantageous when dealing with exponentially growing data volumes.

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Data Lakes Data Warehouse Database Big Data 98

Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

Data Science 52

Data Science SQL Data Scientist Apache Hadoop 52

Smart Data Collective

APRIL 8, 2020

Data warehouse, also known as a decision support database, refers to a central repository, which holds information derived from one or more data sources, such as transactional systems and relational databases. They have undergone significant transformation since then, with modern warehouses housing largescale terabyte capacities.

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Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Sound knowledge of relational databases or NoSQL databases like Cassandra. Hands-on experience working with SQLDW and SQL-DB. Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 1, 2023

Data Engineers work to build and maintain data pipelines, databases, and data warehouses that can handle the collection, storage, and retrieval of vast amounts of data. Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

JULY 24, 2023

” Data management and manipulation Data scientists often deal with vast amounts of data, so it’s crucial to understand databases, data architecture, and query languages like SQL. They often use tools like SQL and Excel to manipulate data and create reports. Specializing can make you stand out from other candidates.

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Pickl AI

FEBRUARY 28, 2023

Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise. Effectively, Data Analysts use other tools like SQL, R or Python, Excel, etc., At length, use Hadoop, Spark, and tools like Pig and Hive to develop big data infrastructures.

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 52

Mlearning.ai

MARCH 15, 2023

Familiarity with Databases; SQL for structured data, and NOSQL for unstructured data. Knowledge of big data platforms like; Hadoop and Apache Spark. Knowledge of big data platforms like; Hadoop and Apache Spark. Experience with machine learning frameworks for supervised and unsupervised learning.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 83

Data Science Analytics Analytics Data Scientist 83

The MLOps Blog

JANUARY 23, 2023

But, it is not rare that data engineers and database administrators process, control, and store terabytes of data in projects that are not related to machine learning. Data from different formats, databases, and sources are combined together for modeling. Basically, every machine learning project needs data. DVC Git LFS neptune.ai

ML 52

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phData

AUGUST 9, 2023

Data was extracted from mainframes and legacy systems into warehouse databases like Oracle and Teradata using custom-built ETL tools. Open source big data tools like Hadoop were experimented with – these could land data into a repository first before transformation. Data volumes exploded as web, mobile, and IoT took off.

ETL 52

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ODSC - Open Data Science

APRIL 26, 2023

Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well. Like their counterparts in the machine learning world, engineers need to know a variety of scripted languages such as SQL for database management, Scala, Java, and of course Python.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Data Science Dojo

JANUARY 12, 2023

Hadoop systems and data lakes are frequently mentioned together. Data is loaded into the Hadoop Distributed File System (HDFS) and stored on the many computer nodes of a Hadoop cluster in deployments based on the distributed processing architecture. Some NoSQL databases are also utilized as platforms for data lakes.

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Data Lakes Data Warehouse Hadoop Machine Learning 221

Data Science 101

JUNE 10, 2019

They are responsible for managing database systems, scaling data architecture to multiple servers, and writing complex queries to sift through the data. Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using. Data Engineers. The Data Science Process.

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Data Science Data Scientist Data Analyst Data Engineering 96

Data Science Blog

MAY 15, 2023

Data Warehouses wurden entwickelt, um strukturierte Daten aus Transaktionssystemen in einem zentralen Repository zu speichern, wo sie mit SQL-basierten Tools bereinigt, umgewandelt und analysiert werden konnten. Mit der zunehmenden Datenmenge und -vielfalt wurde die Verwaltung von Data Warehouses jedoch immer schwieriger und teurer.

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Analytics Vidhya

FEBRUARY 7, 2023

Introduction Data engineering is the field of study that deals with the design, construction, deployment, and maintenance of data processing systems. The goal of this domain is to collect, store, and process data efficiently and efficiently so that it can be used to support business decisions and power data-driven applications.

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Data Engineering Data Engineer Data Engineering Data Engineering 257

Dataconomy

JULY 25, 2023

Apart from formal education, some key skills are crucial for a data scientist: Programming : Proficiency in programming languages like Python, R, SQL, and Java is essential for data manipulation and analysis. If you have the following, especially for you, it can be excellent!

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Data Science Data Scientist Machine Learning Machine Learning 91

Smart Data Collective

MAY 20, 2019

With databases, for example, choices may include NoSQL, HBase and MongoDB but its likely priorities may shift over time. For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. SQL programming skills, specific tool experience — Tableau for example — and problem-solving are just a handful of examples.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Analytics Vidhya

JUNE 27, 2020

Overview There are a plethora of data science tools out there – which one should you pick up? Here’s a list of over 20. The post 22 Widely Used Data Science and Machine Learning Tools in 2020 appeared first on Analytics Vidhya.

Machine Learning 400

Machine Learning Machine Learning Data Science Analytics 400

phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment.

SQL 52

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Smart Data Collective

SEPTEMBER 8, 2021

Extract : In this step, data is extracted from a vast array of sources present in different formats such as Flat Files, Hadoop Files, XML, JSON, etc. Here are few best Open-Source ETL tools on the market: Hadoop : Hadoop distinguishes itself as a general-purpose Distributed Computing platform.

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Smart Data Collective

NOVEMBER 8, 2020

Unlike the old days where data was readily stored and available from a single database and data scientists only needed to learn a few programming languages, data has grown with technology. Understand the Databases. As a data engineer, you will be primarily working on databases. Just like programming, SQL has multiple dialects.

Data Engineering 82

Data Engineering Data Engineer Data Engineering Data Engineering 82

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Tables inherent the key characteristics of its platform BigQuery which provides an upper hand over traditional databases.

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Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Unlike traditional databases that require a predefined schema, Data Lakes accommodate both structured and unstructured data. Here it becomes important to highlight the database systems.

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Data Science Dojo

JULY 24, 2023

Its characteristics can be summarized as follows: Volume : Big Data involves datasets that are too large to be processed by traditional database management systems. databases), semi-structured data (e.g., These datasets can range from terabytes to petabytes and beyond. XML, JSON), and unstructured data (e.g., text, images, videos).

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195

DagsHub

DECEMBER 11, 2023

DVC lacks crucial relational database features, making it an unsuitable choice for those familiar with relational databases. Dolt Created in 2019, Dolt is an open-source tool for managing SQL databases that uses version control similar to Git. Most developers are familiar with Git for source code versioning.

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IBM Journey to AI blog

JANUARY 18, 2023

data platforms and databases), all interacting with one another to provide greater value. A data fabric can consist of multiple data warehouses, data lakes, IoT/Edge devices and transactional databases. Even the transactional databases may also join the fabric network as nodes to offer or consume data assets.

Data Lakes 52

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phData

OCTOBER 20, 2023

Semi-structured data, also called partially structured data, is a form that does not adhere to the conventional tabular structure found in relational databases or other data tables. It is specifically designed to work seamlessly with Hadoop and other big data processing frameworks. What is Semi-structured Data?

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IBM Journey to AI blog

SEPTEMBER 25, 2023

This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success. This allowed them to focus on SQL-based query optimization to the nth degree. They stood up a file-based data lake alongside their analytical database.

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Data Lakes Analytics Analytics Clustering 91

Pickl AI

APRIL 2, 2024

Understanding Data Structured Data: Organized data with a clear format, often found in databases or spreadsheets. SQL (Structured Query Language): Language for managing and querying relational databases. Hadoop/Spark: Frameworks for distributed storage and processing of big data.

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Data Science Machine Learning Machine Learning Predictive Analytics 52