Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

U-Next

MARCH 7, 2023

Taking data from sources and storing or processing it is known as data extraction. Define Data Wrangling The process of data wrangling involves cleaning, structuring, and enriching raw data to make it more useful for decision-making. Data is discovered, structured, cleaned, enriched, validated, and analyzed.

Data Mining 96

Data Mining Scala Government Data Governance 96

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

Towards Data Science

JULY 26, 2024

You might think to yourself, ‘well point one can be solved with {insert data observability tool}’, or ‘point two just needs a stricter data governance plan in place’. For example, there was a project where we needed to automate standardising the raw data coming in from all our clients. That was it.

Data Engineering 75

Data Engineering Data Engineer Engineering Data Warehouse 75

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Cloudera

SEPTEMBER 15, 2022

The data products are packaged around the business needs and in support of the business use cases. This step requires curation, harmonization, and standardization from the raw data into the products. Luke: Let’s talk about some of the fundamentals of modern data architecture. What is a data fabric?

Data Lake 83

Data Lake Analytics Application Cloud Storage Architecture 83

Monte Carlo

JULY 25, 2023

What is data curation? Data curation is the process of transforming and enriching larger amounts of raw data into smaller, more widely accessible subsets of data that provide additional value to the organization or the intended use case. It would also make the data engineering team a bottleneck.

Raw Data 52

Raw Data Data Warehouse Data Architecture 52

RandomTrees

FEBRUARY 6, 2024

Generative AI with Data Lineage: By automating the process of collecting lineage metadata, generating visualizations of data lineage, and identifying and troubleshooting data lineage problems. Generating new insights: Ability to generate new insights from data that would not be possible to find using traditional methods.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

NOVEMBER 14, 2023

Apache Iceberg is an open source data lakehouse table format developed by the data engineering team at Netflix to provide a faster and easier way to process large datasets at scale. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). Limited data type support.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

AUGUST 19, 2023

To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

Building 52

Building BI Data Lake Data Governance 52

Monte Carlo

APRIL 1, 2021

is whether to choose a data warehouse or lake to power storage and compute for their analytics. While data warehouses provide structure that makes it easy for data teams to efficiently operationalize data (i.e., Data discovery tools and platforms can help. Image courtesy of Adrian on Unsplash.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Government 52

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

JANUARY 27, 2023

You have probably heard the saying, "data is the new oil". It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Business Intelligence - ETL is a key component of BI systems for extracting and preparing data for analytics.

BI 52

BI ETL Tools Retail Healthcare 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

dbt Developer Hub

SEPTEMBER 28, 2022

So let’s say that you have a business question, you have the raw data in your data warehouse , and you’ve got dbt up and running. The analyst will try to do as much discovery work up-front as possible, because it’s hard to predict exactly what you’ll find in the raw data. Or are you? Style Guide?).

Engineering 52

Engineering Raw Data Datasets BI 52

Databand.ai

JULY 28, 2022

Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). Other tasks, probably SQL jobs orchestrated with DBT, are running transformation on the loaded data. They are querying raw data tables, enriching them, joining between tables, and creating business data – all ready to be used.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Knowledge Hut

APRIL 25, 2024

They simplify data processing for our brains and give readers a quick overview of past, present, and future performance by helping the user to visualize otherwise complex and weighty raw data. Faster analysis, intuitive dashboards BI solutions are designed to do intense data processing on the cloud or your company's servers.

Business Intelligence 52

Business Intelligence BI Raw Data Data Warehouse 52

Knowledge Hut

OCTOBER 30, 2023

The role of a Power BI developer is extremely imperative as a data professional who uses raw data and transforms it into invaluable business insights and reports using Microsoft’s Power BI. Ensure compliance with data protection regulations. Develop a long-term vision for Power BI implementation and data analytics.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Ascend.io

APRIL 7, 2023

Users derive meaningful business insights by querying the metadata catalog, which pulls specific data from disparate data lakes, warehouses, and lakehouses according to preconfigured data governance standards, sometimes with the help of an AI model.

Metadata 52

Metadata Data Governance Datasets Government 52

Edureka

AUGUST 29, 2024

In this respect, the purpose of the blog is to explain what is a data engineer , describe their duties to know the context that uses data, and explain why the role of a data engineer is central. What Does a Data Engineer Do? Design algorithms transforming raw data into actionable information for strategic decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Generalist 40

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

ProjectPro

JANUARY 31, 2022

As the demand for big data grows, an increasing number of businesses are turning to cloud data warehouses. The cloud is the only platform to handle today's colossal data volumes because of its flexibility and scalability. Launched in 2014, Snowflake is one of the most popular cloud data solutions on the market.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Mutt Data

APRIL 4, 2023

. #3: There Might Not Be A One-Size-Fits-All Solution, But There Are Best Practices To Get You Started We cover just some of the essential best practices to account for before working on your Modern Data Stack. But there are definitely points to look out for: Embrace raw data! Over-formatting can make us lose valuable data.

Raw Data 40

Raw Data Data Warehouse AWS Data 40

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses. They also recently acquired Apache Flink , another streaming solution.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

MAY 12, 2023

There are several widely used unstructured data storage solutions such as data lakes (e.g., MongoDB, Cassandra), and big data processing frameworks (e.g., Also, modern cloud data warehouses and data lakehouses may be good options for the same purposes. Invest in data governance.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Monte Carlo

JULY 27, 2023

It’s not enough for data teams to be magicians transforming raw data into business value, they need to be responsible (and cost conscious) stewards as well. Unfortunately, data optimization is far from an exact science. Typically, the data warehouse, lake, or lakehouse will be a good place to start.

Data 52

Data Architecture Data Warehouse Data Engineering 52