That's why it's good to use this ordering column in the filtering expressions of the query.Īnother type of supported index is Clustered Index that stores data rows ordered by the index key. The CCI can also be ordered to ensure that every segment contains a not overlapping subset of data meaning that it will be either fully skipped or read. Large tables with more than 100 million rows can take full advantage of the compression. It can then rely on column compression to optimize the storage and querying (segment skipped if it doesn't contain relevant data to the query). It's composed of a rowgroup where Synapse stores table data in a column-oriented storage. In addition to the distributions, Synapse also has a concept of indexes, which is not obvious too! By default, Synapse with create a Clustered Columnstore Index (CCI). Below you can find an example of a hash-distributed table on a user_id column, partitioned by user_activity date: ![]() Put another way, the partitions will be distributed. If the table is also partitioned, each distribution will be responsible for distinct partitions. Let's take an example of a hash-distributed table. It concerns the round-robin and hash distribution strategies because the replicated is stored on the compute node. Synapse stores a distributed table across 60 distributions. It took me a while to understand the difference, so let me share it here. In Synapse, you can also use partitions and indexes. Unfortunately, it's not the single aspect. Of course, if we analyzed the data model only with this data distribution aspect, it would be too easy. It distributes the records evenly so that every node gets approximately the same volume of data. round-robin (Synapse), even (Redshift) - good strategy when you don't know the query patterns or want to load the data into a staging table.The engine replicates the whole table in every node. replicated (Synapse), all (Redshift) - good choice for small and not frequently updated tables.hash (Synapse), key (Redshift) - rows with the same key(s) defined in the hash/key distribution are stored together so that in any shuffle operation like joins or group bys, the engine doesn't need to move the data.Hopefully, Synapse and Redshift share the same data distribution methods which are: Data layoutĪnd what happens once we've loaded the data? Since both solutions are distributed, we need to think about organizing the data. It creates an external table on Blob Storage or Data Lake Gen 2 that you can load directly to the Dedicated Pool with SQL. In Synapse, a popular alternative for COPY is PolyBase that favors the ELT approach. There is no connector for CosmosDB, although there is a way to work on CosmosDB data with the Synapse Link feature that creates CosmosDB's copy dedicated for Synapse analytical queries. Also, Synapse's COPY works only with Blob Storage and Data Lake Gen 2. The difference is that Synapse's COPY supports only JSON, CSV, and Parquet, while Redshift's one can also handle the ORC columnar format. Regarding Synapse, it also has a COPY method, with similar features (errors management, compression). The operation has some important options like schema mapping, errors management, or data conversion. We know already what we can do and in what mode, but any data warehouse will be useless without the data! So, how can we bring the data into them? For AWS Redshift, you can use a COPY method to load one or multiple files from your S3 bucket simultaneously. ) formats, Serverless Pool only supports Parquet, CSV, and JSON files. While Spectrum supports both structured (Parquet, Avro, ORC. The difference is the number of supported formats. As for Serverless Pool, you can use it to read your objects from S3. Redshift has a similar serverless component called Redshift Spectrum. Since it's a serverless solution, it's very flexible, and you don't need to manage the hardware. You can use it to read the external tables created on Blob Storage or Data Lake Gen 2. Please notice that I'm talking about Redshift and Dedicated SQL Pool and not about other components of these services.īut it can be a good moment to explain these services too! In addition to the Dedicated SQL Pool, Synapse also has Serverless Pool component. Later, it passes the generated plan for the physical execution to the compute nodes that may exchange any intermediary data via a service bus. In a nutshell, there is a node responsible for getting user query and generating the execution plan. At first glance, one could think that both data warehouses are completely different but in fact, both rely on the Massively Parallel Processing architecture pattern.
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