ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. This is particularly advantageous when dealing with exponentially growing data volumes.

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Women in Big Data

OCTOBER 9, 2024

Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes. Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files.

Big Data 59

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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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Precisely

MARCH 9, 2023

Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

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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

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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Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Data Modelling Data modelling is creating a visual representation of a system or database. Physical Models: These models specify how data will be physically stored in databases.

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Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.

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Becoming Human

JANUARY 23, 2023

Let’s understand with an example if we consider web development so there are UI , UX , Database , Networking , and Servers and for implementing all these things we have different-different tools - technologies and frameworks , and when we have done with these things we just called this process as web development.

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Smart Data Collective

MAY 20, 2019

With databases, for example, choices may include NoSQL, HBase and MongoDB but its likely priorities may shift over time. 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.

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Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing.

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Pickl AI

JANUARY 28, 2025

They encompass all the origins from which data is collected, including: Internal Data Sources: These include databases, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and flat files within an organization. databases), semi-structured (e.g., Data can be structured (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Unlike traditional databases that require a predefined schema, Data Lakes accommodate both structured and unstructured data. Here it becomes important to highlight the database systems.

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Pickl AI

JULY 30, 2024

Below are some prominent use cases for Apache NiFi: Data Ingestion from Diverse Sources NiFi excels at collecting data from various sources, including log files, sensors, databases, and APIs. Its visual interface allows users to design complex ETL workflows with ease. Apache NiFi is used for automating the flow of data between systems.

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Pickl AI

APRIL 6, 2023

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

Azure 52

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DagsHub

NOVEMBER 11, 2024

Unlike structured data, unstructured data doesn’t fit neatly into predefined models or databases, making it harder to analyse using traditional methods. While sensor data is typically numerical and has a well-defined format, such as timestamps and data points, it only fits the standard tabular structure of databases.

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phData

FEBRUARY 25, 2025

With so many different ways to get data into Snowflakefrom traditional ETL tools to APIs, batch processing, and streaming datait can quickly become overwhelming to choose the right approach. In our Hadoop era, we extensively leveraged Apache NiFi to integrate large ERP systems and centralize business-critical data.

Data Pipeline 52

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Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

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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. Tables inherent the key characteristics of its platform BigQuery which provides an upper hand over traditional databases.

SQL 52

SQL Database Apache Hadoop Data Science 52

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.

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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 platforms and databases), all interacting with one another to provide greater value.

Data Lakes 52

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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.

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DagsHub

OCTOBER 23, 2024

Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,

Machine Learning 52

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phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud).

SQL 52

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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 Engineer Data Engineering Data Engineering 40

The MLOps Blog

JANUARY 23, 2023

But, it is not rare that data engineers and database administrators process, control, and store terabytes of data in projects that are not related to machine learning. Data from different formats, databases, and sources are combined together for modeling. Basically, every machine learning project needs data. DVC Git LFS neptune.ai

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Dataconomy

FEBRUARY 23, 2023

This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently. Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data.

Big Data 91

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The MLOps Blog

MAY 31, 2023

So, a better database architecture would be to maintain multiple tables where one of the tables maintains the past 3 months history with session-level details, whereas other tables may contain weekly aggregated click, ATC, and order data. One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data.

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IBM Journey to AI blog

SEPTEMBER 25, 2023

The evolution of Presto at Uber Beginning of a data analytics journey Uber began their analytical journey with a traditional analytical database platform at the core of their analytics. They stood up a file-based data lake alongside their analytical database. Uber has made the Presto query engine connect to real-time databases.

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Pickl AI

MARCH 19, 2025

A data engineer creates and manages the pipelines that transfer data from different sources to databases or cloud storage. Data Storage : Keeping data safe in databases or cloud platforms. It allows them to retrieve, manipulate, and manage structured data in relational databases. What Does a Data Engineer Do?

Data Engineering 52

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