Pickl AI

JANUARY 27, 2025

Hadoop emerges as a fundamental framework that processes these enormous data volumes efficiently. This blog aims to clarify Big Data concepts, illuminate Hadoops role in modern data handling, and further highlight how HDFS strengthens scalability, ensuring efficient analytics and driving informed business decisions.

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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 150

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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

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Smart Data Collective

MAY 20, 2019

For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. 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

phData

AUGUST 9, 2023

This is unlike the more traditional ETL method, where data is transformed before loading into the data warehouse. By bringing raw data into the data warehouse and then transforming it there, ELT provides more flexibility compared to ETL’s fixed pipelines. ETL systems just couldn’t handle the massive flows of raw data.

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Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity. Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 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

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 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

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Pickl AI

NOVEMBER 15, 2023

It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. Through the Extract, Transform, Load (ETL) process, raw and disparate data is transformed into a structured format, making it easily accessible and ready for analysis. What is a Data Lake in ETL?

Data Lakes 52

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Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. What Are Some Common Tools Used in Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

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

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. Enrich your event analytics, leverage advanced ETL operations and respond to increasing business needs more quickly and efficiently.

Apache Kafka 45

Apache Kafka Hadoop ETL Data Pipeline 45

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

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g., Hadoop, Spark).

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. This step often involves: ETL Processes: Extracting, transforming, and loading data into a target system. Read More: Top ETL Tools: Unveiling the Best Solutions for Data Integration.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

IBM Journey to AI blog

JANUARY 18, 2023

While traditional data warehouses made use of an Extract-Transform-Load (ETL) process to ingest data, data lakes instead rely on an Extract-Load-Transform (ELT) process. This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. All phases of the data-information lifecycle.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Alation

JANUARY 25, 2022

For instance, technical power users can explore the actual data through Compose , the intelligent SQL editor. Those less familiar with SQL can search for technical terms using natural language. Tools like an intelligent SQL editor are useful for data prep (at the data integration layer).

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The MLOps Blog

JANUARY 23, 2023

More about Neptune: Working with artifacts: versioning datasets in runs How to version datasets or models stored in the S3 compatible storage Dolt Dolt is a SQL database that is created for versioning and sharing data. With lakeFS it is possible to test ETLs on top of production data, in isolation, without copying anything.

ML 52

ML ML Data Lakes Machine Learning 52

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. is similar to the traditional Extract, Transform, Load (ETL) process. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

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. What is Presto? It also provides features like indexing and caching.”

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

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 Engineering Data Engineering Data Engineer 52