Ascend.io

SEPTEMBER 14, 2023

Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute() compute() Data Storage Python extends its mastery to data storage, boasting smooth integrations with both SQL and NoSQL databases.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Rockset

OCTOBER 20, 2021

This can be helpful when you want to reduce the size of large scale data streams, deduplicate data, or partition your data. Collections can also be created from other data sources including data lakes (e.g. S3 or GCS), NoSQL databases (e.g. DynamoDB or MongoDB), and relational databases (e.g.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

Databand.ai

DECEMBER 13, 2022

Bob also hosts The Engineering Side of Data podcast , which is dedicated to discussions around data engineering and features a variety of guests from the data engineering space. His specialties include Microsoft SQL Server, Azure Databricks, Azure Data Factory, SQL Server Integration Services (SSIS), and Azure Data Lake.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Rockset

JUNE 16, 2020

MongoDB.live took place last week, and Rockset had the opportunity to participate alongside members of the MongoDB community and share about our work to make MongoDB data accessible via real-time external indexing. And the data volumes these applications need to analyze typically scale into multiple terabytes.

MongoDB 52

MongoDB Data Lake PostgreSQL Kafka 52

DareData

JANUARY 30, 2023

For data scientists, these skills are extremely helpful when it comes to manage and build more optimized data transformation processes, helping models achieve better speed and relability when set in production. Examples of NoSQL databases include MongoDB or Cassandra. Introduction to Designing Data Lakes in AWS.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Rockset

AUGUST 25, 2020

In Part One , we discussed how to first identify slow queries on MongoDB using the database profiler, and then investigated what the strategies the database took doing during the execution of those queries to understand why our queries were taking the time and resources that they were taking.

MongoDB 40

MongoDB NoSQL SQL Database 40

ProjectPro

NOVEMBER 14, 2015

Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive. Hadoop Sample Real-Time Project #8 : Facebook Data Analysis Image Source:jovian.ai Business Use Case: The business use case here is to analyze various types of data that are generated on Facebook.

Hadoop 52

Hadoop Project Big Data Healthcare 52

AltexSoft

JUNE 26, 2023

Semi-structured data is not as strictly formatted as tabular one, yet it preserves identifiable elements — like tags and other markers — that simplify the search. They can be accumulated in NoSQL databases like MongoDB or Cassandra. Unstructured data represents up to 80-90 percent of the entire datasphere.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Data Science Blog: Data Engineering

SEPTEMBER 24, 2021

BASE Databases While ACID databases are mostly RDBMS, most other database types, known as NoSQL databases, tend more to conform to BASE principles. Redis, CouchDB, MongoDB, Cosmos DB, Cassandra, ElasticSearch, Neo4J, OrientDB or ArangoDB are just some popular examples. But other than ACID, BASE is not a strict approach.

NoSQL 52

NoSQL Data Warehouse Database MongoDB 52

Rockset

JANUARY 28, 2022

And that was only possible if both internal and external users could drill down into the freshest data possible in order to get the answers they needed. However, Seesaw’s DynamoDB database stored the data in its own NoSQL format that made it easy to build applications, just not analytical ones.

NoSQL 52

NoSQL PostgreSQL MongoDB ETL Tools 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

NOVEMBER 7, 2023

You can swiftly provision infrastructure services like computation, storage, and databases, as well as machine learning, the internet of things, data lakes and analytics, and much more. " Instead of relying on nearby hard drives and personal data centers, it requires storing and accessing data on distant servers.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

AltexSoft

MAY 14, 2021

With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system. Data storage and processing. NoSQL databases. The “NoSQL” part here stands for “Non-SQL” and “Not Only SQL”.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 19, 2022

ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse. Different methods are used to store different types of data. It is better to know when to employ a data lake vs. a data warehouse to create data solutions for an organization.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

OCTOBER 8, 2021

They are applied to retrieve data from the source systems, perform transformations when necessary, and load it into a target system ( data mart , data warehouse, or data lake). So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. Popular data virtualization tools.

Process 69

Process Data Lake Metadata Data Warehouse 69

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

JULY 27, 2021

E.g. Redis, MongoDB, Cassandra, HBase , Neo4j, CouchDB What is data modeling? Data modeling is a technique that defines and analyzes the data requirements needed to support business processes. It is easy to query structured data and perform further analysis on it.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. A loose schema allows for some data structure flexibility while maintaining a general organization. Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

MARCH 14, 2023

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

IT 59

IT Data Warehouse Data Governance Data Lake 59