(Submitting on behalf of a Snowflake User)
We have a database that stores raw data from all our local sources. My team has it's own environment in which we have full permissions to create standardized feeds and/or tables/views etc that is ready to consume through Power BI. A few additional details:
The final 'feed' tables are derived through SQL statements and most are pulling from more than one table of our 'raw' data.
Raw table data is updated daily.
My question is what is the best operation to keep the tables fully updated and what is the standard work flow for this operations? Our current understanding is one of these processes is the best:
Using COPY INTO <stage> then COPY INTO <table>.
Using STREAMS to add incremental data.
Using PIPES (may be the same as STREAMS)
Or simplify our feeds to one table sources and use a materialized view.
We'd ideally like to avoid views to improve consumption speed at the power bi level.
Tasks have been recommended as it seems like a good fit since they only need to update the final table once per day.
(https://docs.snowflake.net/manuals/sql-reference/sql/create-task.html)
Any other recommendations??? THANKS!
We have a similar scenario where we have our raw datalake tables being updated real time from files in S3. Those raw tables are loaded via snowpipe using the auto ingest feature.
In turn, we have a data mart which contains facts about the raw data. To update the data mart, we have created streams on top of the raw tables to track changes. We then use tasks run at a given frequency (every five minutes in our case) to update the data mart from the changed data in the raw tables. Using streams allows us to limit processing to only changed data, without having to track last update dates, etc.
i'm looking for the best database for my big data project.
We are collecting data from some sensors. Every row has about one hundred column.
every day we store some milions of rows.
The most common query is for retreiving data for one sensor in a range of date.
at the moment i use a percona mysql cluster. when i ask data for a range on some days, the response is fast. The problem is when i ask data for a month.
The database is perfectly optimized, but the response time is not acceptable.
I would like to change percona cluster with a database able to perform query in parallel on all the nodes to improve response time.
With Cassandra i could partition data accross nodes (maybe based on the current date) but i have read that cassandra cannot read data between partition in parallel, but i have to create a query for every day. (i don't know why)
Is there a database that manage shard queries automatically, so i can distribute data across all nodes?
With Cassandra, If you split your data across multiple partitions, you still can read data between partition in parallel by executing multiples queries asynchronously.
Cassandra drivers help you handle this, see execute_concurrent from the python driver.
Moreover, the cassandra driver is aware of the data partitioning, it knows which node holds which data. So when reading or writing, it chooses an appropriate node to send the query, according to the driver load balancing policy (specifically with the TokenAwarePolicy).
Thus, the client acts as a load balancer, and your request is processed in parallel by the available nodes.
I am a newbie to Database Systems and I was wondering what is difference between Temporal database and Time-series database. I have searched on the internet but I am not getting any comparison of the two.
A temporal database stores events which happen at a certain time or for a certain period. For example, the address of a customer may change so when you join the invoice table with the customer the answer will be different before and after the move of the customer.
A time-series database stores time-series which are array of number indexed by time. Like the evolution of the temperature with one measure every hour. Or the stock value every second.
Time-series database: A time series database is a database that is optimized to store time-series data. This is data that is stored along with a time stamp so that changes in the data can be measured over time. Prometheus is a time-series database used by Sound Cloud, Docker and Show Max.
Real world uses:
Autonomous trading algorithms, continuously collects data on market changes.
DevOps monitoring stores data of the state of the system over its run time.
Temporal databases contain data that is time sensitive. That is, the data are stored with time indicators such as the valid time (time for which the entry remains valid) and transaction time (time the data was entered into the database). Any database can be used as a temporal database if the data is managed correctly.
Real world uses:
Shop inventory systems keep track of stock quantities, time of purchase and best-before-dates.
Industrial processes that are dependant on valid time data during manufacturing and sales.
I developed industrial software to poll data from devices/RTUs and log these data into relational database every seconds. The HMI of this software allows user to query these data from the database and represent it in the form of table/chart.
Now, this data stored can scale very fast. Normally, there can be easily 100 devices, in which each devices have 100 data, and need to be logged every seconds. We are talking about 100*100*60*60*24 = 864000000 data per day. This industrial software is expected to run 24/7, all year long.
Here's the problem, because of the scale of the data. The querying of the data can be painfully slow. If I were to plot data for 3 months, the SQL query would takes minutes.
My question is, does Hadoop (distributed storage and analytic system) is suitable for my application? Can I leverage the power of Hadoop to speed up the querying of data in my application? How?
Note that the data integrity in my application is very critical.
What is the difference between a database and a data warehouse?
Aren't they the same thing, or at least written in the same thing (ie. Oracle RDBMS)?
Check out this for more information.
From a previous link:
Database
Used for Online Transactional Processing (OLTP) but can be used for other purposes such as Data Warehousing. This records the data from the user for history.
The tables and joins are complex since they are normalized (for RDMS). This is done to reduce redundant data and to save storage space.
Entity – Relational modeling techniques are used for RDMS database design.
Optimized for write operation.
Performance is low for analysis queries.
Data Warehouse
Used for Online Analytical Processing (OLAP). This reads the historical data for the Users for business decisions.
The Tables and joins are simple since they are de-normalized. This is done to reduce the response time for analytical queries.
Data – Modeling techniques are used for the Data Warehouse design.
Optimized for read operations.
High performance for analytical queries.
Is usually a Database.
It's important to note as well that Data Warehouses could be sourced from zero to many databases.
From a Non-Technical View:
A database is constrained to a particular applications or set of applications.
A data warehouse is an enterprise level data repository. It's going to contain data from all/many segments of the business. It's going to share this information to provide a global picture of the business. It is also critical to integration between the different segments of the business.
From a Technical view:
The word "Data Warehouse" has been given no recognized definition. Personally, I define a data warehouse as a collection of data-marts. Where each data-mart consists of one or more databases where the database is specific to a specific problem set (application, data-set or process).
Simply put a database is a component of a data-warehouse. There are many places to explore this concept, but because there is no "definition", you will find challenges with any answer you give.
A data warehouse is a TYPE of database.
In addition to what folks have already said, data warehouses tend to be OLAP, with indexes, etc. tuned for reading, not writing, and the data is de-normalized / transformed into forms that are easier to read & analyze.
Some folks have said "databases" are the same as OLTP -- this isn't true. OLTP, again, is a TYPE of database.
Other types of "databases": Text files, XML, Excel, CSV..., Flat Files :-)
The simplest way to explain it would be to say that a data warehouse consists of more than just a database. A database is an collection of data organized in some way, but a data warehouse is organized specifically to "facilitate reporting and analysis". This however is not the entire story as data warehousing also contains "the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system".
Data Warehouse
Data Warehouse vs Database: A data warehouse is specially designed for data analytics, which involves reading large amounts of data to understand relationships and trends across the data. A database is used to capture and store data, such as recording details of a transaction.
Data Warehouse:
Suitable workloads - Analytics, reporting, big data.
Data source - Data collected and normalized from many sources.
Data capture - Bulk write operations typically on a predetermined batch schedule.
Data normalization - Denormalized schemas, such as the Star schema or Snowflake schema.
Data storage - Optimized for simplicity of access and high-speed query. performance using columnar storage.
Data access - Optimized to minimize I/O and maximize data throughput.
Transactional Database:
Suitable workloads - Transaction processing.
Data source - Data captured as-is from a single source, such as a transactional system.
Data capture - Optimized for continuous write operations as new data is available to maximize transaction throughput.
Data normalization - Highly normalized, static schemas.
Data storage - Optimized for high throughout write operations to a single row-oriented physical block.
Data access - High volumes of small read operations.
DataBase :-
OLTP(online transaction process)
It is current data, up-to-date detailed data, flat relational
isolated data.
Entity relationship is used to design the database
DB size 100MB-GB simple transaction or quires
Datawarehouse
OLAP(Online Analytical process)
It is about Historical data Star schema,snow flexed schema and galaxy
schema is used to design the
data warehouse
DB size 100GB-TB Improved query performance foundation
for DATA MINING DATA VISUALIZATION
Enables users to gain a deeper understanding and knowledge about various
aspects of their corporate data through fast, consistent, interactive access
to a wide variety of possible views of the data
Any data storage for application generally uses the database. It could be relational database or no sql databases which are currently trending.
Data warehouse is also database. We can call data warehouse database as specialized data storage for the analytical reporting purposes for the company.
This data used for key business decision.
The organized data helps is reporting and taking business decision effectively.
Database:
Used for Online Transactional Processing (OLTP).
Transaction-oriented.
Application oriented.
Current data.
Detailed data.
Scalable data.
Many Users, Administrators / Operational.
Execution time: short.
Data Warehouse:
Used for Online Analytical Processing (OLAP).
Oriented analysis.
Subject oriented.
Historical data.
Aggregated data.
Static data.
Not many users, manager.
Execution time: long.
A Data Warehousing (DW) is process for collecting and managing data from varied sources to provide meaningful business insights. A Data warehouse is typically used to connect and analyze business data from heterogeneous sources. The data warehouse is the core of the BI system which is built for data analysis and reporting.
Source for the Data warehouse can be cluster of Databases, because databases are used for Online Transaction process like keeping the current records..but in Data warehouse it stores historical data which are for Online analytical process.
A Data Warehouse is a type of Data Structure usually housed on a Database. The Data Warehouse refers the the data model and what type of data is stored there - data that is modeled (data model) to server an analytical purpose.
A Database can be classified as any structure that houses data. Traditionally that would be an RDBMS like Oracle, SQL Server, or MySQL. However a Database can also be a NoSQL Database like Apache Cassandra, or an columnar MPP like AWS RedShift.
You see a database is simply a place to store data; a data warehouse is a specific way to store data and serves a specific purpose, which is to serve analytical queries.
OLTP vs OLAP does not tell you the difference between a DW and a Database, both OLTP and OLAP reside on databases. They just store data in a different fashion (different data model methodologies) and serve different purposes (OLTP - record transactions, optimized for updates; OLAP - analyze information, optimized for reads).
See in simple words :
Dataware --> Huge data using for Analytical/storage/ copy and Analysis .
Database --> CRUD operation with Frequently used data .
Dataware house is Kind of storage which u are not using on daily basis & Database is something which your dealing frequently .
Eg. If we are asking statement of bank then it gives us for last 3/4/6/more months bcoz it is in database. If you want more than that it stores on Dataware house.
Example: A house is worth $100,000, and it is appreciating at $1000 per year.
To keep track of the current house value, you would use a database as the value would change every year.
Three years later, you would be able to see the value of the house which is $103,000.
To keep track of the historical house value, you would use a data warehouse as the value of the house should be
$100,000 on year 0,
$101,000 on year 1,
$102,000 on year 2,
$103,000 on year 3.