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

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 337

Apache Hadoop Hadoop Data Warehouse SQL 337

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

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 266

Apache Kafka Apache Hadoop Big Data Big Data 266

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 […].

Hadoop 271

Hadoop Data Warehouse Data Engineering Data Engineer 271

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 280

Database Hadoop Data Science Analytics 280

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

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 Decision Trees Python 195

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

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

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 74

Big Data Big Data Apache Hadoop Hadoop 74

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

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 Apache Hadoop 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 Analyst Data Science Machine Learning 172

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

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 132

Data Warehouse Big Data Big Data Business Intelligence 132

Pickl AI

MARCH 24, 2023

Effectively, Data Science Bootcamps help you learn various languages important in Data Science like Python, Python Libraries, Hadoop, R, SQL and Spark. Accordingly, some of these benefits include the following: It teaches you to learn various languages like Python, Pandas, Java, Scala, Hadoop, SQP, R, etc.

Data Science 52

Data Science Data Scientist Data Analysis Data Analysis 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

ODSC - Open Data Science

SEPTEMBER 27, 2023

Cost-Efficiency By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. Processing: Relational databases are optimized for transactional processing and structured queries using SQL.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

FEBRUARY 28, 2023

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. Accordingly, they work with different data types, including sales figures, customer data, financial records and market research data.

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. Experience with machine learning frameworks for supervised and unsupervised learning. Experience with cloud platforms like; AWS, AZURE, etc.

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 84

Data Science Analytics Analytics Data Scientist 84

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

Data Science 101

JUNE 10, 2019

Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using. Among the skills necessary to become a data scientist include an analytical mindset, mathematics, data visualization, and business knowledge, just to name a few. Each tool plays a different role in the data science process.

Data Science 96

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.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

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

phData

AUGUST 9, 2023

Open source big data tools like Hadoop were experimented with – these could land data into a repository first before transformation. All while having an extremely low barrier to entry, only requiring SQL to begin to get comfortable, and a Pythonic templating engine known as Jinja allows you to template and automate various models.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

SAS Software

DECEMBER 6, 2023

本ブログでは、このような結果に陥らないために意識すると良いと思われることをお伝えしていきます。 もっとも簡略化したデータマネージメントの歴史 アナリティクスに特化したデータマネージメント考察の第一期ーHadoopの到来 このころまではダッシュボードや定型レポート用にRDBMSやデータベースアプライアンスが構えられるのみで、アナリティクス用途としてはSASデータセットやフラットファイルでの運用が主でした。これはアナリティクス的なデータ加工ワークロードに適したテクノロジーがSAS以外にはなかったからです。Hadoopの登場により、アナリティクス用途でのデータ活用が一気に拡大し、パフォーマンスやスケーラビリティの制約から解放された一方で、従来の目的を先に決めてデータマートを先に設計して、という従来の方法論では、アナリティクスによる効果創出が最大化されないという課題も見えてきました。 (..)

Hadoop 52

Hadoop SQL AI AI 52

IBM Journey to AI blog

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques.

Machine Learning 60

Machine Learning Machine Learning Data Science Big Data 60

Analytics Vidhya

MARCH 8, 2023

The Biggest Data Science Blogathon is now live! Knowledge is power. Sharing knowledge is the key to unlocking that power.”― Martin Uzochukwu Ugwu Analytics Vidhya is back with the largest data-sharing knowledge competition- The Data Science Blogathon.

Data Science 334

Data Science Analytics Analytics Apache Hadoop 334

O'Reilly Media

SEPTEMBER 15, 2021

When we looked at the most popular programming languages for data and AI practitioners, we didn’t see any surprises: Python was dominant (61%), followed by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). Salaries by Programming Language. What about Kafka?

AI 145

AI AI Azure AWS 145

Analytics Vidhya

JANUARY 2, 2023

Introduction You must have noticed the personalization happening in the digital world, from personalized Youtube videos to canny ad recommendations on Instagram. While not all of us are tech enthusiasts, we all have a fair knowledge of how Data Science works in our day-to-day lives. All of this is based on Data Science which is […].

Data Engineering 315

Data Engineering Data Engineer Data Engineering Data Engineering 315

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.

Data Engineering 306

Data Engineering Data Engineer Data Engineering Data Engineering 306

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

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!

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91