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

MARCH 19, 2025

Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Python, SQL, and Apache Spark are essential for data engineering workflows. SQL Structured Query Language ( SQL ) is a fundamental skill for data engineers.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

OCTOBER 27, 2024

Students learn to work with tools like Python, R, SQL, and machine learning frameworks, which are essential for analysing complex datasets and deriving actionable insights1. Big Data Technologies: Familiarity with tools like Hadoop and Spark is increasingly important.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

IBM Journey to AI blog

AUGUST 29, 2024

Event-driven businesses across all industries thrive on real-time data, enabling companies to act on events as they happen rather than after the fact. This is where Apache Flink shines, offering a powerful solution to harness the full potential of an event-driven business model through efficient computing and processing capabilities.

Apache Kafka 45

Apache Kafka Hadoop ETL Data Pipeline 45

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

Smart Data Collective

MAY 20, 2019

The entire process is also achieved much faster, boosting not just general efficiency but an organization’s reaction time to certain events, as well. For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. Data processing is another skill vital to staying relevant in the analytics field.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

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 94

Data Science Analytics Analytics Data Scientist 94

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

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

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

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

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

ODSC - Open Data Science

JANUARY 7, 2025

While knowing Python, R, and SQL is expected, youll need to go beyond that. Similar to previous years, SQL is still the second most popular skill, as its used for many backend processes and core skills in computer science and programming. Employers arent just looking for people who can program.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

AUGUST 23, 2024

These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents. Other Apache Griffin is an open-source data quality solution for big data environments, particularly within the Hadoop and Spark ecosystems. It is SQL-based and integrates well with modern data warehouses.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

How to Learn Machine Learning

MAY 3, 2025

Cloud Functions Create serverless applications that trigger predictions based on events. Dataproc Process large datasets with Spark and Hadoop before feeding them into your ML pipeline. Data Preparation with Cloud Storage and BigQuery High-quality data is the foundation of any successful ML model.

Machine Learning 59

Machine Learning Machine Learning AI AI 59