Analysts Corner

JUNE 18, 2023

ETL Developer: Defining the Role An ETL developer is a professional responsible for designing, implementing, and managing ETL processes that extract, transform, and load data from various sources into a target data store, such as a data warehouse. Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

Data Warehouse 130

Data Warehouse Data Quality Documentation Data Governance 130

Cprime

MAY 4, 2023

Key Data Integration Use Cases Let’s focus on the four primary use cases that require various data integration techniques: Data ingestion Data replication Data warehouse automation Big data integration Data Ingestion The data ingestion process involves moving data from a variety of sources to a storage location such as a data warehouse or data lake.

Data Warehouse 52

Data Warehouse Big Data Finance Data Quality 52

Astera

MARCH 7, 2024

ETL testing is a set of procedures used to evaluate and validate the data integration process in a data warehouse environment. In other words, it’s a way to verify that the data from your source systems is extracted, transformed, and loaded into the target storage as required by your business rules.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Modelling 52

Astera

APRIL 27, 2023

The modern data stack (MDS) is a collection of tools for data integration that enable organizations to collect, process, store and analyze data. Being based on a well-integrated cloud platform, modern data stack offers scalability, efficiency, and proficiency in data handling.

Data Warehouse 52

Data Warehouse Big Data Cloud Computing Data Analytics 52

Astera

JANUARY 2, 2024

Free Download Here’s what the data management process generally looks like: Gathering Data: The process begins with the collection of raw data from various sources. Once collected, the data needs a home, so it’s stored in databases, data warehouses , or other storage systems, ensuring it’s easily accessible when needed.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

The BAWorld

SEPTEMBER 2, 2021

Non-technical users can also work easily with structured data. Structured Data Example. can be grouped in a data warehouse for marketing analysis. This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. of organizations are investing in big data.

Big Data 52

Big Data Data Warehouse Data Visualization Visualization 52

Actian

MAY 21, 2019

The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.

Big Data 40

Big Data Data Warehouse Artificial Intelligence Digital Transformation 40

Astera

AUGUST 22, 2023

Data Warehousing AI Select: This feature aids you in identifying potential Fact and Dimension tables from selected entities. By leveraging AI capabilities, it automatically determines the appropriate classification, streamlining the data modeling process for entities with uncertain categorization.

Innovation 52

Innovation Documentation Data Analytics Data Analytics 52

Sisense

JULY 14, 2019

If you just felt your heartbeat quicken thinking about all the data your company produces, ingests, and connects to every day, then you won’t like this next one: What are you doing to keep that data safe? Data security is one of the defining issues of the age of AI and Big Data. Understanding Your Users.

Data Security 75

Data Security Data Modelling Cybersecurity Documentation 75

Sisense

MAY 27, 2019

Often with a background in advanced mathematics and/or statistical analysis, data scientists conduct high-level market and business research to help identify trends and opportunities, and then, to summarize, these findings are presented by the business analyst to the business and stakeholders in a manner that aids decision-making.

Skillset 75

Skillset Cloud Computing Big Data Visualization 75

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

Data Modelling 84

Data Modelling Big Data Data Analytics Data Analytics 84

Data Pine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 96

Data Quality Monitoring Big Data Government 96

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

Data Warehouse 216

Data Warehouse Data Quality Data Analytics Data Analytics 216

Data Pine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis.

Business Intelligence 95

Business Intelligence Business Intelligence Data Visualization Visualization 95

Smart Data Collective

JUNE 16, 2022

Data is fed into an Analytical server (or OLAP cube), which calculates information ahead of time for later analysis. A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on. Types: HOLAP stands for Hybrid Online Analytical Processing.

Data Warehouse 327

Data Warehouse Data Modelling Monitoring Business Intelligence 327

Sisense

JANUARY 15, 2021

This is one of the reasons we’ve seen the rise of data teams — they’ve grown beyond Silicon Valley startups and are finding homes in Fortune 500 companies. As data has become more massive, the technical skills needed to wrangle it have also increased. Situation #2: Established company creates a data team for deeper insights.

Skillset 75

Skillset Data Warehouse Data Modelling Business Intelligence 75

Sisense

FEBRUARY 24, 2021

Data space dimension: Traditional data vs. big data. This dimension focuses on what type of data the CDO has to wrangle. Traditional datasets are often relational data found at the core of transactional services and operations: Think of an accounting system or point-of-sale system that spans multiple locations.

Chief Data Officer 69

Chief Data Officer Data Warehouse Big Data Innovation 69

Sisense

JUNE 26, 2019

It was a big investment that is still viable, and one that forms the legacy databases of many companies today. . Fast forward to Big Data. Now we can throw in the four 4 V’s of Big Data (Variety, Volume, Velocity, and Veracity) and compound the data issues of the enterprise with an even bigger data issue.

Data Warehouse 75

Data Warehouse Data Modelling Big Data Monitoring 75

Sisense

JULY 22, 2020

They hold structured data from relational databases (rows and columns), semi-structured data ( CSV , logs, XML , JSON ), unstructured data (emails, documents, PDFs), and binary data (images, audio , video). Sisense provides instant access to your cloud data warehouses. Connect tables.

Digital Transformation 98

Digital Transformation Data Warehouse IBM Support IBM cost 98

Sisense

APRIL 7, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. We live in an era of Big Data. The sheer amount of data being generated is greater than ever (we hit 18 zettabytes in 2018) and will continue to grow.

Big Data 86

Big Data Data Warehouse Digital Transformation Product Management 86

Insight Software

NOVEMBER 21, 2023

To have any hope of generating value from growing data sets, enterprise organizations must turn to the latest technology. You’ve heard of data warehouses, and probable data lakes, but now, the data lakehouse is emerging as the new corporate buzzword. To address this, the data lakehouse was born.

IBM Maintenance 52

IBM Maintenance Data Warehouse IBM cost Supply Chain 52

Insight Software

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. It is organized to create a top-down model that is used for analysis and reporting. Ideally, your primary data source should belong in this group.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance IBM cost 52