High gas prices strain budgets of hunger relief groups



Two people putting groceries into a vehicle

Soaring gasoline prices over the last few months have forced many Minnesotans to make difficult choices, some of them deciding whether to buy food or fill the tank. And while pump prices have eased some in recent weeks from their peak of a statewide average of $4.35 a gallon a month ago, the pump price is still much higher than it was before the start of the war with Iran, leaving many wondering when, if ever, there will truly be relief.

A shopping cart loaded with groceries
A shopping cart loaded with groceries to be delivered to a client of ECHO Food Shelf on June 18. Those without transportation, limited mobility and medical conditions rely on home delivery services the food shelf provides when they can't make it in person.
Hannah Yang | MPR News

This week, the state’s average price for a gallon of regular unleaded gas is hovering around $3.70, which is still below the national average of $3.93 per gallon, according to AAA. But it is still a dollar a gallon more than it was in January.

For those who were already struggling to make ends meet, the spike in gas prices since the beginning of the war with Iran further strains their household budgets.

“People usually tend to cut on their food expenses before they cut back on anything else, so that's kind of one of the first things that goes,” said Deisy De Leon Esqueda, director of ECHO Food Shelf in Mankato. “And then after that, it’s maybe like their prescription drugs or gas.”

“[Gas prices have] risen, but like you have to get to work,” Esqueda added. “So there’s really no other option besides that.”

A person loading up a vehicle with groceries
Deisy De Leon Esqueda, ECHO Food Shelf manager loads up her car to make a delivery in rural Blue Earth County on June 18.
Hannah Yang | MPR News

That has increased the need for people to rely on food shelves like ECHO to help stock their fridges and put food on their tables. But the rising cost of gasoline and diesel fuel is also beginning to strain the budgets of these nonprofit groups that help feed people across Minnesota.

ECHO Food Shelf has a grocery delivery service called ECHO Delivers. It relies on volunteers to deliver food packages and other essentials to those who physically can’t come into the food shelf themselves.

A person putting groceries in a vehicle
Deisy De Leon Esqueda, manager of ECHO Food Shelf, loads up her car with groceries and other necessities to make a home delivery on June 18.
Hannah Yang | MPR News

“People that are on Echo Delivers typically have had some type of surgery or [they’re] not, are not able to get around,” said Esqueda, while delivering some groceries to a client herself. “Or maybe they're not driving anymore, or just have very limited mobility, and, and that's where we come in.”

Bruce Piltz is one of those volunteer drivers. The 73-year old from Mankato started volunteering to deliver groceries for ECHO about eight months ago.

“It’s essential,” Piltz told MPR News. “Grocery prices are so high, and so by being able to deliver to people who can’t [and] don’t have transportation, or simply can’t get out, we’re able to help them get by every month.”

Piltz makes his deliveries on Tuesdays and said he’s on-call when needed. And he said he covers a wide area of rural Blue Earth County.

A woman driving
Gas prices are dropping, but the last few months of high costs put a strain on volunteers who make deliveries and also clients who are choosing between fuel or food. Deisy De Leon Esqueda says food is often the first thing people cut from their budget in able to afford gas to commute to work.
Hannah Yang | MPR News

“I've been over as far as Faribault, I've been over to St. James in that Madelia surrounding area, so I've got a range,” Piltz said.

Piltz receives gas vouchers from ECHO Food Shelf to help reimburse him for his gas and mileage costs, and he said he personally is OK with absorbing some of the extra expenses for making those deliveries. But he said the rising costs aren’t unnoticed, and he’s needed to cut back on other days to save money.

“I realized something had to give,” Piltz said. “What that meant was doing less driving (on other days). Plus, when I did go out and run errands or go shopping, I’ve always tried to schedule three or four stops on one outing, that way I can conserve on gas.”

Hunger relief groups feeling the strain

Minnesotans made more than 9 million food shelf visits in 2025, a record number, according to The Food Group, a Twin Cities-based hunger relief nonprofit. And the demand isn’t slowing down, as the organization expects this year will be the fifth year in a row that the number of food shelf visits in the state sets a new record.

Already feeling that strain from the increasing demand for their services, which has been compounded by federal budget cuts to the Supplemental Nutrition Assistance Program, or SNAP, hunger relief organizations are now feeling the impact of higher gas prices on their budgets.

Sophia Lenarz-Coy, executive director of The Food Group, said the nonprofit fuel costs have soared 23 percent since the beginning of the year. In January, the organization’s fuel costs were $3,877 per month and by May, it had jumped almost $1,000 a month, to $4,783.

Lenarz-Coy said The Food Group’s third-party carriers also charged higher fuel surcharges. One vendor they used to pay 25 percent as a surcharge got bumped up to 45 percent.

A person holding groceries
Deisy De Leon Esqueda, manager of ECHO Food Shelf, delivers groceries to rural clients in Judson, Minn. who aren’t able to make it to the food shelf in person on June 18.
Hannah Yang | MPR News

“These added costs are getting baked in at different levels of the food supply chain,” Lenarz-Coy said. “We are starting to see our third-party carriers and vendors increase their delivery fees to us. We’re not doing that right now to our partners, but that is just kind of added cost pressure that’s showing up.”

Other hunger relief nonprofits are also feeling the pinch in sourcing food and reimbursing their volunteers too. Toni Scott, chief supply chain officer at Second Harvest Heartland, said the fuel costs have increased nearly 25 to 40 percent just from January through March of this year, and that was before gas prices peaked in mid-May.

These increased prices may also affect the variety of food visitors to their local food shelf might see. Scott said to reduce transportation costs, they shifted to source more of their produce from local and regional farms and orchards. Last year nearly 12 million pounds of produce came from about 130 Minnesota growers.

“We’re hoping that it does stay more of a short term issue,” Scott said. “It’s something that we’ve been able to manage. Should pricing like this continue in the long term, we would have to look more closely at our overall budget to make sure that we are not impacting the amount of food into the community, but looking for other ways to offset the pressures coming from those price increases.”



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Power BI Datasets – Table of Content

What is Power BI?

Power BI is a set of software services, apps, and connectors that work together to turn disparate data sources into coherent, visually immersive, and interactive insights. Your data could be in the form of an Excel spreadsheet or a hybrid data warehouse that is both on-premises and cloud-based. Power BI makes it simple to connect to your data sources, visualize and uncover what matters, and share your findings with whomever you choose.

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What are Datasets in Power BI?

A dataset is a data collection that you can connect to or import. Power BI allows you to connect to and import all kinds of datasets, allowing you to put everything together in one place. Dataflows can also be used for sourcing the data for Datasets. Workspaces are associated with datasets, and a single dataset can be used in multiple workspaces.
We have selected “My workspace” and then the “Datasets + dataflows” tab in the example below

Power BI workspace

Let us now look into the different types of Datasets in Power BI.

Types of Datasets

Datasets in Power BI are ready to report and visualize the source of data. There are five different types of datasets, each of which can be constructed in one of the following ways:

  • An existing data model will be connected that is not hosted in a Power BI capability.
  • Power BI Desktop file needs to be uploaded which includes a model.
  • Uploading a CSV (comma-separated values) file, or uploading an Excel workbook (Includes one or more Excel tables and/or a workbook data model).
  • Creating a push dataset using the Power BI service.
  • Creating streaming or dataset with hybrid streaming using the Power BI service.

Let us now explore different types of Datasets.

1) External-hosted models

Azure Analysis Services and SQL Server Analysis Services are the two types of externally hosted models. Installing the on-premises data gateway, whether on-premises or VM-hosted infrastructure-as-a-service (IaaS), is required to connect to a SQL Server Analysis Services model. A gateway isn’t required for Azure Analysis Services.

When there are existing model investments, such as those that form part of an enterprise data warehouse(EDW), connecting to Analysis Services makes sense. By utilizing the identity of the Power BI report user, Power BI can establish a live connection to Analysis Services, enforcing data permissions. Both tabular models and multidimensional (cubes) are supported by SQL Server Analysis Services. A live connection dataset sends queries to externally hosted models, as demonstrated in the accompanying 

External-hosted models

2) Power BI Desktop-developed models

A model can be created using Power BI Desktop, a client application for Power BI development. The model is essentially a tabular Analysis Services model. Models can be created by importing data from dataflows and blending it with data from external sources. While the characteristics of how modeling can be accomplished are outside the subject of this article, it’s crucial to note that Power BI Desktop supports three different types, or modes, of models. We are going to discuss the datasets in the coming sections.

Row-Level Security (RLS) can be used in externally hosted models and Power BI desktop models to restrict the amount of data that can be obtained for a certain user. Users in the Salespeople security group, for instance, can only see report data for the sales region(s) to which they’ve been assigned. Roles in RLS can be either static or dynamic. Static roles apply the same filters to all users allocated to the position, whereas dynamic roles filter by the report user.

3) Excel workbook models

The creation of a model is automatic when datasets are created from Excel workbooks or CSV files. To construct model tables, Excel tables, and CSV data are imported, and an Excel workbook data model is translated to produce a Power BI model. In every scenario, data from a file is imported into a model.

4) Push Dataset

A Power BI dataset that can only be created and populated using the Power BI API is known as a push dataset. However, the lack of a good user interface for creating a push dataset restricted its adoption to scenarios where a single table was inhabited with real-time data streaming.

5) Hybrid Streaming Dataset

Real-time streaming in Power BI allows you to stream data and update dashboards in real-time. Real-time data and visuals can be displayed and updated in any Power BI visual or dashboard. Factory sensors, social media sources, service usage metrics, and a variety of other time-sensitive data collectors or transmitters can all be used to collect and transmit streaming data.

Hybrid Streaming Dataset

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How to Create a Power BI Dataset?

Before discussing the steps of creation. It is necessary to know that there are three basic ways to retrieve data in Power BI Desktop that you will use to create your visualizations:

1) Live:

Here you will be connecting to a server that carries all the data. Although no data is sent, the model’s metadata is imported into Power BI Desktop. A query is transmitted to the server when you build visualizations, and it is then executed. The outcomes are then visualized and returned to Desktop. With SQL Server Analysis Services (SSAS) models, whether multidimensional or Tabular, live connections are commonly employed. Power BI Desktop behaves like any other thin client in this scenario, like Excel or Reporting Services (SSRS). It is not possible to make major modifications to the model, but you can add new measurements that will be available in that  .pbix file.

2) DirectQuery:

You can make more modifications to the model here than you can with a Live connection. The data is kept on the server, and queries are run on the server, just like in Live. The Power BI Desktop model, for instance, allows for the creation of relationships.

3) Import:

Power Query queries are used to import the data into a Power BI Desktop file (.pbix). The data is compressed highly so it’s feasible to load records in millions into a file on your system. A model, comparable to an SSAS Tabular model, is built behind the scenes. This is the most versatile mode, as it allows you to blend data from any source. However, all data must be loaded into your model, which can take a long time to refresh.

Now, let’s move to create the dataset. Below are the steps which make you comprehend the creation of the Power BI Dataset.

1) A dataset is connected to the .pbix file where it was created one by one. When you first launch PBI Desktop, click “Get Data” to create a new dataset.

Get Data

Alternatively, you can choose a source from the dropdown menu as shown below:

dropdown menu

2) Let’s assume we imported a few tables from the WideWorldImporters SQL Server sample database (The .pbix file can be downloaded here). The tables and their relationships are visible in the Model view:

.pbix file downloaded

3) You can view the actual data of one table at a time in the “Data view”.

Data view

4) You can create, view, and interact with visualizations built on top of the data and model in the “Report view”. 

Report view

 The dataset is made up of the data as well as the model view. Now, let’s move to the different modes of Dataset available in Power BI.  

[ Related Article : msbi ]

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Dataset modes in the Power BI

These modes of Dataset in Power BI ascertain whether or not data is imported into the model or retained in the data source. The following are the three Dataset modes in Power BI:

  1. Import
  2. DirectQuery
  3. Composite
1) Import

The most popular mode for developing datasets is the import mode. Because of in-memory querying, this mode provides incredibly quick performance. Modelers can also benefit from design flexibility and support for certain Power BI service capabilities (Quick Insights, Q&A, etc.). It’s the default mode when developing a new Power BI Desktop solution because of these advantages.

It’s crucial to realize that all imported data is saved on disk. When the data is refreshed or queried, it should be fully loaded into the memory of Power BI. Import models can yield very rapid query results once they are in memory. It’s also crucial to note that there’s no such thing as a partially loaded Import model in memory. An Import model can also integrate data from any number of supported data source types. The following image illustrates it. 

Import model

2) DirectQuery

Import mode can be replaced by DirectQuery mode. Data is not imported into models created in DirectQuery mode. Instead, they are made up entirely of metadata that defines the model’s structure. If the model is queried, data is retrieved by using the native queries from the underlying data source.

DirectQuery Model

3) Composite

The composite mode can blend DirectQuery and Import modes, or integrate multiple data sources for DirectQuery. The storage mode for every model table can be configured for models created in Composite mode. Calculated tables (defined with DAX) can also be used in this mode.

Composite Model

Import and DirectQuery modes are used in composite models to give you the best of both modes. They can blend the high query performance of in-memory models with the capacity to access near real-time data from data sources when set properly.

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 Conclusion:
We have successfully learned that Power BI lets you connect various datasets for importing and bringing them all together in one place. In this blog, we explored the topics of Datasets in Power BI in a systematic flow by understanding Power BI, then Datasets in Power BI, different types of Datasets and models used for reporting and visualizing data, creating a Dataset for connecting files, and various modes of Datasets in Power BI.

Related Article:

  1. MSBI vs Power BI
  2. Looker vs Power BI
  3. KPI in Power BI
  4. DAX In Power BI
  5. Power BI Architecture
  6. Power BI Components
  7. Power BI Dashboard
  8. Power BI Data Modeling
  9. Power BI Documentation



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