Analytics Vidhya

OCTOBER 28, 2021

The official description of Hive is- ‘Apache Hive data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and […].

Apache Hadoop 343

Apache Hadoop Data Warehouse Hadoop SQL 343

Pickl AI

JUNE 18, 2023

Hadoop has become a highly familiar term because of the advent of big data in the digital world and establishing its position successfully. However, understanding Hadoop can be critical and if you’re new to the field, you should opt for Hadoop Tutorial for Beginners. What is Hadoop? Let’s find out from the blog!

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Dataconomy

APRIL 21, 2016

Recent technology advances within the Apache Hadoop ecosystem have provided a big boost to Hadoop’s viability as an analytics environment—above and beyond just being a good place to store data. Leveraging these advances, new technologies now support SQL on Hadoop, making in-cluster analytics of data in Hadoop a reality.

Hadoop Analytics 132

Hadoop Analytics Hadoop Apache Hadoop Analytics 132

phData

APRIL 26, 2024

One common scenario that we’ve helped many clients with involves migrating data from Hive tables in a Hadoop environment to the Snowflake Data Cloud. Click Create cluster and choose software (Hadoop, Hive, Spark, Sqoop) and configuration (instance types, node count). Configure security (EC2 key pair). Find ElasticMapReduce-master.

Hadoop 52

Hadoop Clustering AWS Database 52

Analytics Vidhya

JULY 12, 2023

Best Big Data Softwares - Apache Hadoop, Apache Spark, apache Kafka, Apache Storm, Apache Cassandra, Apache Hive, zoho & more.

Apache Kafka 234

Apache Kafka Apache Hadoop Big Data Big Data 234

Data Science Dojo

JULY 6, 2023

Apache Hadoop: Apache Hadoop is an open-source framework for distributed storage and processing of large datasets. Hadoop consists of the Hadoop Distributed File System (HDFS) for distributed storage and the MapReduce programming model for parallel data processing.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

Data Science Connect

JANUARY 27, 2023

Learn SQL: As a data engineer, you will be working with large amounts of data, and SQL is the most commonly used language for interacting with databases. Understanding how to write efficient and effective SQL queries is essential.

Data Engineering 130

Data Engineering Data Engineer Data Engineering Data Engineering 130

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 […].

Data Warehouse 265

Data Warehouse Hadoop Data Engineering Data Engineer 265

Analytics Vidhya

MAY 17, 2022

It is developed as a part of the Hadoop ecosystem and runs on top of HDFS. This article was published as a part of the Data Science Blogathon. HBase is an open-source non-relational, scalable, distributed database written in Java. It provides random real-time read and write access to the given data. It is possible to […].

Database 257

Database Hadoop Data Science Analytics 257

Data Science Dojo

AUGUST 27, 2024

Python, R, and SQL: These are the most popular programming languages for data science. Hadoop and Spark: These are like powerful computers that can process huge amounts of data quickly. Python, R, and SQL: These are the most popular programming languages for data science. Statistics provides the language to do this effectively.

Data Scientist 274

Data Scientist Data Science Machine Learning Machine Learning 274

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.

Data Lakes 222

Data Lakes Data Warehouse Hadoop Machine Learning 222

Towards AI

SEPTEMBER 28, 2023

With PySpark, you can write Python and SQL-like commands to manipulate and analyze data in a distributed processing environment. It leverages Apache Hadoop for both storage and processing. Apache Spark: Apache Spark is an open-source data processing framework for processing large datasets in a distributed manner.

Apache Hadoop 75

Apache Hadoop Hadoop Python SQL 75

Data Science Dojo

AUGUST 27, 2024

Python, R, and SQL: These are the most popular programming languages for data science. Hadoop and Spark: These are like powerful computers that can process huge amounts of data quickly. Python, R, and SQL: These are the most popular programming languages for data science. Statistics provides the language to do this effectively.

Data Scientist 195

Data Scientist Data Science Machine Learning Machine Learning 195

Data Science Dojo

OCTOBER 31, 2024

Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Programming Questions Data science roles typically require knowledge of Python, SQL, R, or Hadoop. Their role is crucial in understanding the underlying data structures and how to leverage them for insights.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

Data Science Dojo

JULY 7, 2023

Tools such as Python, R, and SQL help to manipulate and analyze data. Data scientists need a strong foundation in statistics and mathematics to understand the patterns in data. Proficiency in tools like Python, R, SQL, and platforms like Hadoop or Spark is essential for data manipulation and analysis.

Data Science 195

Data Science Data Scientist Python Decision Trees 195

Pickl AI

NOVEMBER 4, 2024

Various types of storage options are available, including: Relational Databases: These databases use Structured Query Language (SQL) for data management and are ideal for handling structured data with well-defined relationships. Apache Spark Spark is a fast, open-source data processing engine that works well with Hadoop.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

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.

Hadoop 52

Hadoop SQL Database Data Analyst 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.

Big Data 76

Big Data Big Data Apache Hadoop Hadoop 76

Pickl AI

JULY 25, 2023

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. Big Data Technologies: Hadoop, Spark, etc. ETL Tools: Apache NiFi, Talend, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

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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ETL Hadoop Data Warehouse Data Pipeline 99

Pickl AI

OCTOBER 22, 2024

With SQL support and various applications across industries, relational databases are essential tools for businesses seeking to leverage accurate information for informed decision-making and operational efficiency. SQL enables powerful querying capabilities for data manipulation.

Database 52

Database SQL Big Data Big Data 52

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 195

Data Science Machine Learning Machine Learning Data Visualization 195

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

Data Science Dojo

JULY 24, 2023

Hadoop Distributed File System (HDFS) : HDFS is a distributed file system designed to store vast amounts of data across multiple nodes in a Hadoop cluster. Spark provides a high-level API in multiple languages like Scala, Python, Java, and SQL, making it accessible to a wide range of developers.

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195

Pickl AI

JUNE 7, 2024

Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Microsoft SQL Server Integration Services (SSIS) Microsoft SQL Server Integration Services (SSIS) is an enterprise-level platform for data integration and transformation. How to drop a database in SQL server?

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

MAY 23, 2023

Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Check out this course to upskill on Apache Spark —  [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 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. Machine learning Machine learning is a key part of data science.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Smart Data Collective

APRIL 8, 2020

This data is then processed, transformed, and consumed to make it easier for users to access it through SQL clients, spreadsheets and Business Intelligence tools. The company works consistently to enhance its business intelligence solutions through innovative new technologies including Hadoop-based services.

Data Warehouse 135

Data Warehouse Big Data Big Data Business Intelligence 135

Pickl AI

APRIL 6, 2023

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

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 1, 2023

Hadoop: The Definitive Guide by Tom White This comprehensive guide delves into the Apache Hadoop ecosystem, covering HDFS, MapReduce, and big data processing. Key Benefits & Takeaways: Master Python’s data processing capabilities, making you proficient in data cleaning, wrangling, and exploration.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

FEBRUARY 20, 2020

Alation catalogs and crawls all of your data assets, whether it is in a traditional relational data set (MySQL, Oracle, etc), a SQL on Hadoop system (Presto, SparkSQL,etc), a BI visualization or something in a file system, such as HDFS or AWS S3. With Alation, you can search for assets across the entire data pipeline.

SQL 52

SQL Hadoop Data Pipeline Analytics 52

Smart Data Collective

NOVEMBER 8, 2020

As such, you should begin by learning the basics of SQL. SQL is an established language used widely in data engineering. Just like programming, SQL has multiple dialects. Besides SQL, you should also learn how to model data. As a data engineer, you will be primarily working on databases.

Data Engineering 83

Data Engineering Data Engineer Data Engineering Data Engineering 83

Pickl AI

MAY 29, 2024

Programming Languages (Python, R, SQL) Proficiency in programming languages is crucial. SQL is indispensable for database management and querying. The curriculum covers data extraction, querying, and connecting to databases using SQL and NoSQL. Python and R are popular due to their extensive libraries and ease of use.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

phData

FEBRUARY 5, 2024

The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment. Instead of manually converting these queries, consider using software built to automate the translation of queries from your legacy systems language to Snowflake’s version, such as phData’s SQL Translation Tool.

SQL 52

SQL Database Data Quality Data Warehouse 52

IBM Journey to AI blog

AUGUST 29, 2024

Integration: Integrates seamlessly with other data systems and platforms, including Apache Kafka, Spark, Hadoop and various databases. With its easy-to-use and no-code format, users without deep skills in SQL, Java, or Python can leverage events, enriching their data streams with real-time context, irrespective of their role.

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Apache Kafka Hadoop ETL Data Pipeline 45

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. Views Views in GCP BigQuery are virtual tables defined by SQL query that can display the results of a query or be used as the base for other queries.

SQL 52

SQL Database Apache Hadoop Data Science 52