Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. Understanding the ETL Process. Before you understand what is ETL tool , you need to understand the ETL Process first. Types of ETL Tools.

ETL 93

ETL Hadoop Data Warehouse Data Pipeline 93

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. This is particularly advantageous when dealing with exponentially growing data volumes.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Becoming Human

JANUARY 23, 2023

Big Data has the ETL (pipelining), Data engineering , Hadoop , Data Warehousing , and Data Mining whereas Data Science has Mathematics, Machine learning, Deep Learning, Computer Vision, NLP, RL, AIOps, Data Reporting, Dashboarding , and all.

Data Science 103

Data Science Big Data Big Data Deep Learning 103

Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. This includes Database System Management (SQL or Non-SQL), Data Warehousing, Machine Learning, programming basics, and ETL. Sound knowledge of relational databases or NoSQL databases like Cassandra.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Analytics Vidhya

FEBRUARY 27, 2023

Introduction Enterprises here and now catalyze vast quantities of data, which can be a high-end source of business intelligence and insight when used appropriately. Delta Lake allows businesses to access and break new data down in real time.

Data Lakes 240

Data Lakes Business Intelligence Business Intelligence Analytics 240

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. Professionals adept at this skill will be desirable by corporations, individuals and government offices alike.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

JANUARY 23, 2023

With lakeFS it is possible to test ETLs on top of production data, in isolation, without copying anything. Also, lakeFS can be used for data management, ETL testing, reproducibility for experiments, and CI/CD for data to prevent future failures.

ML 52

ML ML Data Lakes Machine Learning 52

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

Data Lakes Data Warehouse Database ETL 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

phData

FEBRUARY 5, 2024

Matillion Matillion is a complete ETL tool that integrates with an extensive list of pre-built data source connectors, loads data into cloud data environments such as Snowflake, and then performs transformations to make data consumable by analytics tools such as Tableau and PowerBI. Get to know all the ins and outs of your upcoming migration.

SQL 52

SQL Database Data Quality Data Warehouse 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. You can use stored procedures to handle complex ETL processes, make API calls, and perform data validation.

SQL 52

SQL Database Apache Hadoop Data Science 52

Dataconomy

FEBRUARY 23, 2023

This involves working with various tools and technologies, such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, to move data from its source to its destination. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

Alation

JANUARY 25, 2022

On the process side, DataOps is essentially an agile and unified approach to building data movements and transformation pipelines (think streaming and modern ETL). These approaches extend the continuum of enterprise data warehouses, federated data marts, big data (Hadoop), and virtualization on top of distributed cloud file storage.

DataOps 52

DataOps SQL ML ML 52

The MLOps Blog

MAY 31, 2023

To store Image data, Cloud storage like Amazon S3 and GCP buckets, Azure Blob Storage are some of the best options, whereas one might want to utilize Hadoop + Hive or BigQuery to store clickstream and other forms of text and tabular data. One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data.

ML 59

ML ML Algorithm Machine Learning 59

Alation

FEBRUARY 20, 2020

Business Intelligence used to require months of effort from BI and ETL teams. More recently, we’ve seen Extract, Transform and Load (ETL) tools like Informatica and IBM Datastage disrupted by self-service data preparation tools. You used to be able to get those standards from your colleague in the BI/ETL team.

ETL 52

ETL Hadoop Tableau Business Intelligence 52

Alation

FEBRUARY 20, 2020

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. However, most are only deployed over one data store (Hadoop or other various backends).

Data Warehouse 52

Data Warehouse Hadoop ETL Data Science 52

IBM Journey to AI blog

SEPTEMBER 25, 2023

As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries. It can ingest data from offline batch data sources (such as Hadoop and flat files) as well as online data sources (such as Kafka).

Data Lakes 90

Data Lakes Analytics Analytics Clustering 90