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DCI John Luther is a near-genius murder detective whose brilliant mind can't always save him from the dangerous violence of his passions.
Teresa flees Mexico after her drug-runner boyfriend is murdered. Settling in Dallas she looks to become the country's reigning drug smuggler and to avenge her lover's murder.
Due to a political conspiracy, an innocent man is sent to death row and his only hope is his brother, who makes it his mission to deliberately get himself sent to the same prison in order to break the both of them out, from the inside.
A biker struggles to balance being a father and being involved in an outlaw motorcycle club. An ex-con assumes the identity of a murdered sheriff in the small town of Banshee, Pennsylvania, where he has some unfinished business.
A chronicled look at the criminal exploits of Colombian drug lord Pablo Escobar, as well as the many other drug kingpins who plagued the country through the years.
Patrick, a man trying to escape his role as a drug kingpin for a legitimized life in the elite of NYC; Joseph Sikora as his brother in arms in the drug business, Tommy Egan; Naturi Naughton plays Ghost's fierce wife Tasha St.
Patrick; and Lela Loren is his first love, Assistant U. Attorney Angela Valdes. Written by Starz. Overall this show was great!
But they ruined the ending just as bad as the Game of Thrones ending. The last episode has the lowest rating in the history of the entire show.
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Crazy Credits. Alternate Versions. Rate This. Episode Guide. James "Ghost" St. Patrick, a wealthy New York night club owner who has it all, catering to the city's elite and dreaming big, lives a double life as a drug kingpin.
Creator: Courtney A. Added to Watchlist. Top-Rated Episodes S5. Error: please try again. Series a ver. How Much Have You Seen?
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Episodes Seasons. Edit Cast Series cast summary: Omari Hardwick James 'Ghost' St. Patrick 63 episodes, Lela Loren Angela Valdes 63 episodes, Naturi Naughton Tasha St.
Patrick 63 episodes, Joseph Sikora Tommy Egan 63 episodes, Shane Johnson Cooper Saxe 63 episodes, Rotimi Dre 55 episodes, Michael Rainey Jr.
Tariq St. Patrick 53 episodes, La La Anthony Joe Proctor 48 episodes, 50 Cent Kanan 44 episodes, J. Julio 36 episodes, Michael J Ferguson Mike Sandoval 30 episodes, Larenz Tate Councilman Tate 29 episodes, Andy Bean Greg 28 episodes, Luis Antonio Ramos Ruiz 28 episodes, Lucy Walters Cheat Sheet: "Power" Season 5.
First let's understand how these datasets differ from one another this section , then we discuss how to push data into those each of these datasets.
With a push dataset , data is pushed into the Power BI service. When the dataset is created, the Power BI service automatically creates a new database in the service to store the data.
Since there is an underlying database that continues to store the data as it comes in, reports can be created with the data.
These reports and their visuals are just like any other report visuals, which means you can use all of Power BI's report building features to create visuals, including Power BI visuals, data alerts, pinned dashboard tiles, and more.
Once a report is created using the push dataset, any of its visuals can be pinned to a dashboard.
On that dashboard, visuals update in real-time whenever the data is updated. Within the service, the dashboard is triggering a tile refresh every time new data is received.
With a streaming dataset , data is also pushed into the Power BI service, with an important difference: Power BI only stores the data into a temporary cache, which quickly expires.
The temporary cache is only used to display visuals which have some transient sense of history, such as a line chart that has a time window of one hour.
With a streaming dataset , there is no underlying database, so you cannot build report visuals using the data that flows in from the stream.
As such, you cannot make use of report functionality such as filtering, Power BI visuals, and other report functions.
The only way to visualize a streaming dataset is to add a tile and use the streaming dataset as a custom streaming data data source.
The custom streaming tiles that are based on a streaming dataset are optimized for quickly displaying real-time data.
There is very little latency between when the data is pushed into the Power BI service and when the visual is updated, since there's no need for the data to be entered into or read from a database.
In practice, streaming datasets and their accompanying streaming visuals are best used in situations when it is critical to minimize the latency between when data is pushed and when it is visualized.
In addition, it's best practice to have the data pushed in a format that can be visualized as-is, without any additional aggregations. Examples of data that's ready as-is include temperatures, and pre-calculated averages.
Because this call is made from the web client directly, you would have to list traffic to PubNub as allowed, if you only allowed whitelisted outbound traffic from your network.
Please refer to the instructions in the support article about whitelisting outbound traffic for PubNub. As with the streaming dataset , with the PubNub streaming dataset there is no underlying database in Power BI, so you cannot build report visuals against the data that flows in, and cannot take advantage of report functionality such as filtering, Power BI visuals, and so on.
As such, the PubNub streaming dataset can also only be visualized by adding a tile to the dashboard, and configuring a PubNub data stream as the source.
Tiles based on a PubNub streaming dataset are optimized for quickly displaying real-time data. Since Power BI is directly connected to the PubNub data stream, there is very little latency between when the data is pushed into the Power BI service and when the visual is updated.
The following table or matrix, if you like describes the three types of datasets for real-time streaming, and lists capabilities and limitations of each.
See this article for information on Push limits on how much data can be pushed in. The previous section described the three primary types of real-time datasets you can use in real-time streaming, and how they differ.
This section describes how to create and push data into those datasets. If no defaultMode flag is set, the dataset defaults to a push dataset.
If the defaultMode value is set to pushStreaming , the dataset is both a push and streaming dataset, providing the benefits of both dataset types.
When using datasets with the defaultMode flag set to pushStreaming , if a request exceeds the 15Kb size restriction for a streaming dataset, but is less than the 16MB size restriction of a push dataset, the request will succeed and the data will be updated in the push dataset.
However, any streaming tiles will temporarily fail. When creating the new streaming dataset, you can select to enable Historic data analysis as shown below, which has a significant impact.
When Historic data analysis is disabled it is disabled by default , you create a streaming dataset as described earlier in this article. When Historic data analysis is enabled , the dataset created becomes both a streaming dataset and a push dataset.
In such datasets, the dataset owner receives a URL with a rowkey, which authorizes the requestor to push data into the dataset with out using an Azure AD OAuth bearer token.
This section describes technical details about how that process occurs. During creation of the dataset, Azure Stream Analytics also sets the retentionPolicy flag to basicFIFO ; with that setting, the database supporting its push dataset stores , rows, and after that limit is reached, rows are dropped in a first-in first-out FIFO fashion.
If your Azure Stream Analytics query results in very rapid output to Power BI for example, once or twice per second , Azure Stream Analytics will begin batching those outputs into a single request.
This may cause the request size to exceed the streaming tile limit. In that case, as mentioned in previous sections, streaming tiles will fail to render.
In such cases, the best practice is to slow the rate of data output to Power BI; for example, instead of a maximum value every second, set it to a maximum over 10 seconds.
Now that we've covered the three primary types of datasets for real-time streaming, and the three primary ways you can push data into a dataset, let's get your real-time streaming dataset working in Power BI.
To get started with real-time streaming, you need to choose one of the two ways that streaming data can be consumed in Power BI:. With either option, you'll need to set up Streaming data in Power BI.
To do this, in your dashboard either an existing dashboard, or a new one select Add a tile and then select Custom streaming data.
If you don't have streaming data set up yet, don't worry - you can select manage data to get started.
On this page, you can input the endpoint of your streaming dataset if you already have one created into the text box. The next section describes these options, and goes into more detail about how to create a streaming tile or how to create a dataset from the streaming data source, which you can then use later to build reports.
There are two ways to create a real-time streaming data feed that can be consumed and visualized by Power BI:. When you select API from the New streaming dataset window, you're presented with entries to provide that enable Power BI to connect to and use your endpoint:.
If you want Power BI to store the data that's sent through this data stream, enable Historic data analysis and you'll be able to do reporting and analysis on the collected data stream.
You can also learn more about the API. For example, wrap your JSON objects in an array. When you select PubNub and then select Next , you see the following window:.
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